PhD Dissertation

International Doctorate School in Information and
Communication Technologies

DISI - University of Trento

Study and Development of Novel
Techniques for PHY-Layer Optimization
of Smart Terminals in the Context of
Next-Generation Mobile Communications

Leandro D'Orazio

Advisor:

Prof. Claudio Sacchi

Università degli Studi di Trento

November 2008

Abstract

Future mobile broadband communications working over wireless channels are required to
provide high performance services in terms of speed, capacity, and quality. A key issue
to be considered is the design of multi-standard and multi-modal ad-hoc network architec-
tures, capable of self-conguring in an adaptive and optimal way with respect to channel
conditions and trac load. In the context of 4G-wireless communications, the implemen-
tation of ecient baseband receivers characterized by aordable computational load is a
crucial point in the development of transmission systems exploiting diversity in dierent
domains. This thesis proposes some novel multi-user detection techniques based on dier-
ent criterions (i.e., MMSE, ML, and MBER) particularly suited for multi-carrier CDMA
systems, both in the single- and multi-antenna cases. Moreover, it considers the use of
evolutionary strategies (such as GA and PSO) to channel estimation purposes in MIMO
multi-carrier scenarios. Simulation results evidenced that the proposed PHY-layer opti-
mization techniques always outperform state of the art schemes by spending an aordable
computational burden.
Particular attention has been used on the software implementation of the formulated algo-
rithms, in order to obtain a modular software architecture that can be used in an adaptive
and optimized recongurable scenario.

Prof. The work described in this thesis would not be possible
without his assistance.
Last. but not least. Dr. Jérôme Louveaux for all their help during the period I spent in
Louvain-la-Neuve. My thanks also to all the other members in the Laboratoire de Télé-
communications et Télédétection. Especially
°
c
the members of the ELEctromagnetic DIAgnostic (ELEDIA ) and Multimedia Signal
Processing and Understanding (mmLAB) laboratories. my brother
Daniele and my whole family for their help and support. Alfonso and Teresa.
I would also like to thank my other co-authors on various papers. all the people I played with and
for.
I am very grateful to Prof. Luc Vandendorpe for the opportunity to visit his research
group at Université Catholique de Louvain (UCL).. Fabrizio Granelli. Francesco G. Prof.
Leandro D'Orazio
i
.
I gratefully acknowledge all my former and present colleagues at the Information Engi-
neering and Computer Science Department (DISI) of the University of Trento.Acknowledgements
Foremost.. Claudio Sacchi for his guidance and
support over the past three years. I would like to thank my advisor Prof. De Natale for
interesting and rewarding co-operation. Belgium. I am also very grateful
to him and to Prof. and all my real friends. I would like to thank my parents. Massimo Donelli and Prof. wherever they are.
A special thanks to my current TRITon colleagues. Riccardo Fedrizzi. B.

The rst-generation (1G) was analog-based. A variety of services have been oered in such a context. wireless mobile
systems have begun to permeate all areas of our daily life and are therefore required to
provide high-speed. continuing to Data and now to Multimedia. providing even several benets to the users
in terms of mobility and exibility in the placement of terminals. This was followed in Europe in 1981 by the Nordic Mobile Telephone (NMT450)
system developed by Ericsson for Scandinavia. Signicant reductions in cost and time
can also be achieved using wireless solutions. starting from
Voice. In fact. given that the operating frequency was dierent in almost
1
.Chapter 1
Introduction
Wireless communication is one of the most active areas of research over the past and the
current decades. Japan by Nippon Telephone and Telegraph
(NTT). It used analog modulations and sent in-
formation as a continuously varying waveform. in 1982 by the Total Access Communi-
cations System (TACS) for the United Kingdom and in 1985 by the Extended Total
Access Communications System (ETACS) for the other european countries. The widespread commercial deployment
of 1G systems started in the 1979s in Tokyo. This evolution has been made possible by academic
and industrial Research and Development (R&D) labs with the implementation of three
generations of cellular systems ([1]). Those sys-
tems were not interoperable. high-capacity and high-quality services with performances closer to
those aorded by wireline systems.

. voice and low bit-rate data). where only a single analog
cellular standard called Advanced Mobile Phone System (AMPS) has been developed by
Ameritech in 1983. unied standard in Europe. and multimedia (playing music. web browsing. with the development of new technologies always based
on the classical GSM (e.
but when demands for a variety of wideband services increased (e. providing
an upgraded transmission technology with a single.
The second generation (2G) cellular systems use digital modulation schemes. viewing videos. These advances helped improve user
capacity.
lms. and error correction coding techniques. The situation was dierent in the United States. high speed Internet
access and video/high quality images transmission). IS-136) in 1992 and 1996. The rst step in this direction has been commonly accepted as the second
and one-half generation (2. North
American Spread Spectrum System (IS-95) in 1993 ([6]) and Japan Digital Cellular (JDC)
in 1992 ([7]).and high-bandwidth
services like telephony (voice. which has been recognized
by the International Telecommunication Union (ITU) as the most promising candidate
for the international 3G standard under the International Mobile Telecommunications
.5G). etc. video.
The real 3G mobile systems have been developed to oer both low. The spreading of 2G systems started
with the european Global System for Mobile communications (GSM) in 1990 ([5]) and
continued with North American TDMA Systems (IS-54.g. voice quality. The rst
3G system was introduced in October 2001 in Japan by NTT DoCoMo under the name
FOMA (Freedom of Mobile Multimedia Access) ([8]).). and spectrum eciency. e-commerce. television. Internet access (e-mail. GSM became the more widely used system throughout the world. 2 Introduction
each country. 3. fax.
videoconferencing.. General Packet Radio Service (GPRS) and Enhanced Data
Rates for Global Evolution (EDGE)). the evolution toward third generation
(3G) started. etc.). at any time and from anywhere through a single device. etc. source
coding. These
systems were able to perfectly provide basic services (e.g. 4]).g.. It is based on the Wideband Code
Division Multiple Access (WCDMA) transmission protocol.). These systems suered from low user capacity and had voice quality
issues ([2.

At the present time. it is predicted that the current 3G technology and its rst evolu-
tions (commonly recognized as the third and one-half generation (3. launched on Au-
gust 2001 by the founding members Alcatel. such as wireless Internet
services (with peak rate of 384 Kbps) and video transmissions (with data rate up to
2Mbps). with premium quality
and high security. and Digital Video Broadcasting (DVB). and Siemens) has
dened the following as objectives of the 4G wireless communication standard ([9. It should be able to meet needs of future high-performance applications
like wireless broadband access. The goal is to have data rates in the range 50-100 Mbps in
cellular networks and around 1 Gbps in the WLAN environment. mobile
TV. video chat. The resulting technology will be therefore based on a fully IP-
based integrated system. Motorola. 13]):
• a spectral ecient system
• high network capacity: more simultaneous users per cell
• a nominal data rate of 100 Mbps while the client physically moves at high speeds
relative to the station. The 4G tendency
is to integrate mobile communications standards (specied by IMT) and Wireless Local
Area Networks (WLAN).5G)) will tend to be
congested in few years. Ericsson.
The 4G working group (the Wireless World Research Forum (WWRF). Nokia. IMT-2000. and 1 Gbps while client and station are in relatively xed
positions as dened by the ITU-R
• a data rate of at least 100 Mbps between any two points in the world
• smooth hando across heterogeneous networks
.Introduction 3
programme. 10. Multimedia Messaging Service (MMS). High-Denition TeleVision (HDTV) content.
It requires an extension of the 3G capacity by an order of magnitude.
12. The research community and the telecommunications industry are
therefore working to identify possible solutions for the fourth generation (4G) of wireless
communications. It is able to oer wideband services. 11.

g. The user-centric system demonstrates that it is mandatory in
the design of 4G to focus on the upper layers (max user-sensitivity) before improving or
developing the lower ones.
The research community has already generated several enabling technologies for 4G. such as user personalization.2 ([9]). Without user friendliness.1: The user-centric system presented in [14]
• seamless connectivity and global roaming across multiple networks
• high quality of service for next generation multimedia support
• interoperability with existing wireless standards
• an all IP. in [14]. for example. the further the planet is from the center of the system the less
the user is sensitive to it. and ultra wideband radio. multi-
carrier modulation.
e. adaptive coding and modulation.1. multi-user detection. 1. multiple antennas and Multiple Input Multiple Output (MIMO) channels. packet switched network.
.
An innovative vision of 4G systems has been provided by Frattasi et al.. 4 Introduction
Figure 1.
The road to the fourth generation is pictorially summarized in Fig. the user is located in the center of the system and the dierent key
features dening 4G rotate around him on orbits with a distance dependent on a user-
sensitivity scale. the user cannot exploit
his device and access to other features. Therefore. space-time
coding. As
depicted in Fig. iterative (turbo) decoding algorithms. 1.

Countermeasures
should be employed in order to combat these impairments and to achieve high-speed
communications. high-quality communications and/or high-capacity communications. and co-channel interference.2: Evolution of wireless communication systems
The subject of this thesis is inserted in such a framework.
A widely considered solution consists of the use of diversity in the frequency. fading.
Frequency diversity can be obtained by transmitting the information-bearing signal by
means of several carriers that are spaced suciently apart from each other to provide
independently fading versions of the signals. This may be accomplished by choosing a
.
1. it is necessary to con-
sider the hostile physical properties of the wireless channel in the form of rapid time
variation. multipath propagation.1 The Motivations
In order to implement reliable wireless communication networks. time.1 The Motivations 5
Figure 1. and
space domains. considering the study and
development of novel techniques for physical layer optimization of diversity based smart
terminals in the context of next-generation wireless communications.1.

It has been
shown ([17]) that the link capacity can be increased by m = min (NT x . all of the energy provided by the
channel will be extracted. Multiple
users are supported by allowing each user to transmit over the same N subcarriers at the
same time by using a unique spreading sequence (typically +1/-1 values) to separate the
various users at the receiver. Hence. In this way. independent information streams can be delivered on the m parallel
spatial channels to realize the increased transmission bit rate (called spatial multiplexing)
or one can deliver the same information bits over multiple spatial channels to exploit the
. because there are m spatial channels created as a result
of the multiple antennas and the scattering environment surrounding the transmitter and
the receiver.
Time diversity can be obtained by transmitting the information-bearing signal in dierent
time slots. NRx ) times relative
to single-antenna wireless links. By using a Rake receiver. a user sends simultaneously his data stream over N subcarriers. it is possible to demodulate each
path of the composite multipath signal. 6 Introduction
frequency spacing equal or larger than the coherence bandwidth of the channel.
The transmission technique that exploits this natural diversity in the time domain is
Direct-Sequence Code-Division Multiple Access (DS-CDMA) with a Rake receiver ([15]).
Space diversity can be obtained by using multiple transmit or receive antennas. The advantage of having NT x and NRx
multiple antennas at the transmitter and the receiver respectively is to transform the
original wireless fading channels into MIMO wireless fading channels ([16]).
In MC-CDMA.
In DS-CDMA.
with the spacing between adjacent antennas being chosen so as to ensure the independence
of possible fading events occurring in the channel.
The most common techniques actually used to exploit the frequency diversity are based
on multi-carrier modulation: Orthogonal Frequency Division Multiplexing (OFDM) and
Multi-Carrier Code-Division Multiple Access (MC-CDMA) ([15]). with the interval between successive time slots being equal to or grater than
the coherence time of the channel. each symbol of the data stream of each user is modulated by a known and
unique spreading sequence. or both.

i. Some of these transmission techniques have been already used as standard for
the second and third generations of mobile communications systems and the others are
currently considered as possible solutions for the future generations. time.
and MIMO systems (space). In the rst case.
.. DS-CDMA (time).
In Fig.e. and more reliable transmission techniques.3: Wireless communications techniques overview
spatial diversity so as to enhance the reliability of the transmission. these basic techniques are partially or en-
tirely combined to obtain new.
and space diversity) can be exploited separately or jointly. That is the reason
why these techniques and several aspects concerning them have been widely analyzed in
the literature.
It is evident that the most general case is represented by MIMO-MC-DS-CDMA and
all the other cases can be obtained by removing one or more degrees of freedom from
this one.
The three types of diversity obtainable in multipath fading channels (frequency. it is possible to nd a summary of the obtainable techniques. more ecient.3. 1.1 The Motivations 7
Figure 1.1. In the second case. the aforemen-
tioned techniques are used. MC-CDMA or OFDM (frequency).

On the contrary.
This approach is completely dierent from the traditional Minimum Mean Square Er-
ror (MMSE) criterion: the receiver aims to minimize directly the BER rather than the
Mean Square Error (MSE) ([18]). and multi-carrier modulations
(both OFDM and MC-CDMA).e. the optimization of the radio link is
a key issue to be faced. it is possible to inte-
grate the space. it is possible to mitigate the eects of the
multipath propagation and to reject interference: both RF co-channel interference coming
from other transmitters and baseband interferences (i. Using this kind of approach. In this way. This thesis work jointly considers the diversity in space.g. 8 Introduction
1.
The Multi-User Detection (MUD) problem has been even handled by considering the
. time. interchannel interference of OFDM systems. From an algorithmic
point of view. In particular.
Direct-Sequence Spread Spectrum (DS-SS) modulation. considering that MBER approach has not been yet considered in
MIMO MC-CDMA systems.. Usually. and
frequency domain. and frequency diversities obtained. the problem of the antenna arrays optimization and the problem
of the baseband reception are separately considered in the current literature using dier-
ent algorithms.2 The Problems and the Innovative Solutions
In the context of 4G wireless communications systems. In this work. in order to provide performances very close to the single-user bound
in multipath fading channels. and multi-user interference in CDMA
systems). e. intersymbol interference. time. it will be introduced into the multi-user detection of Space
Time Block Coded (STBC) MIMO MC-CDMA signals. the novelty of the considered approach relies on the joint solution of the
beamforming problem and the ecient reception for wireless systems equipped with an-
tenna arrays. clipping
noise. new strategies for the joint optimization of
antenna arrays and baseband receiver sections have been developed. the proposed methodology aims to found an integrated
approach able not only to maximize the Signal-to-Interference-plus-Noise-Ratio (SINR)
at the antenna arrays output but even to minimize simultaneously the Bit Error Rate
(BER) at the output of the baseband receiver (both for single carrier and multi-carrier
modulations).. by using smart antennas.

These approaches are very
ecient from a computational point of view. This draw-
back makes them more suitable for static channels rather than for time varying fading
channels ([20]).
Both the proposed multi-user detection and channel estimation techniques have been
. the Particle Swarm Optimization (PSO)
algorithm. their performances and conver-
gence rates are strongly inuenced by the choice of the updating parameters. The PSO is a stochastic evolutionary computation technique developed by
Kennedy and Eberhart in 1995 ([25]).and PSO-assisted MMSE estimate of
the channel matrix has been considered. They are characterized by robustness. it is often preferred in
practical applications of MC-CDMA due to its reduced computational load and because
it can easily support adaptive implementations based on Least Mean Square (LMS) or
Recursive Least Square (RLS) optimization algorithms ([19]). the fading channel coecients are not known to the receiver. Their use has been also investigated in order to implement a computa-
tionally tractable Maximum Likelihood (ML) MUD detector for synchronous multi-rate
MC-CDMA systems. Despite its intrinsic sub-optimality.
adaptation capability and reduced sensitivity to parameter setting ([21]). They
are explicitly estimated by the receiver and the estimate is used as if it were the exact
Channel State Information (CSI). inspired by social behavior of bird ocking or sh
schooling.
The GAs have been even used for the Channel Impulse Response (CIR) estimation. in particular for diversity based 4G-systems. This technique
was pioneered by Holland in the 60's and 70's and his work is comprehensively presented
in [22]. while useful practical details of genetic algorithms are available in [23. In such a context.1. are commonly recognized
in the literature as valuable optimization tools.2 The Problems and the Innovative Solutions 9
conventional MMSE criterion.
In real systems. In this work. 24]. even if it is only a mathematical estimation of what is
truly happening in nature. an evolutionary computing method. the application of evolutionary methodologies for optimiza-
tion like Genetic Algorithms (GAs) has been considered to implement a semi-adaptive
MMSE MUD reception in MC-CDMA systems transmitting information over multipath
fading channels. However. GAs. GA. in
conjunction with another evolutionary strategy.

M. R. L. An adaptive minimum-
BER approach for multi-user detection in STBC-MIMO MC-CDMA systems. Genetic
algorithm-based MMSE receiver for MC-CDMA systems transmitting over time-
varying mobile channels. L. D'Orazio. G. the GA-assisted MUD approach described in section 3.
1. Sacchi. Donelli. MMSE multi-user
.
Therefore. 172173.
The adaptive MBER receiver for STBC MIMO MC-CDMA mobile communication sys-
tems proposed in section 3. De Natale. Electronics Letters . vol. Fedrizzi. De Natale.
2007. in Proc. M. 34273431. Donelli. in order to obtain an adaptive and optimized
PHY-layer recongurability. of GLOBECOM 2007 Conf. C. 10 Introduction
implemented by taking into account their possible application in fully recongurable ter-
minals capable of adapting their transmission layer to a change of status of the network. Sacchi.1 has been presented in
one conference paper and one journal paper:
• C. Donelli. B. Feb. D'Orazio. B. they have been developed by considering adaptive design issues such as modu-
larity and re-usability of software modules. in
Proc. B. Sacchi. In
particular. M. 2007. C. 2006.. G. 43. of PIMRC 2006 Conf. pp. and F. D'Orazio. and F. De Natale. R. De Natale.3 has been presented in one conference paper:
• L. Sept.. Fedrizzi.1
have been published in one conference paper:
• L. Sacchi. pp. 3. Nov.3 Research Contributions
Some parts of this thesis have been already published in international publications. D'Orazio. and F.
• C. G. G. B.
The results obtained by using the channel estimation technique presented in section 4. R. A genetic
algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile
communication systems. no. Fedrizzi. and F.

Chapter 6 presents the experimental results obtained for the proposed techniques.
Chapter 7 concludes the dissertation summarizing the obtained results and introducing
suggestions for future works. C. D'Orazio.
Chapter 5 takes into account the possibility to use the presented algorithms in an
adaptive and optimized recongurable system. D'Orazio. F.
. Moreover. gives an overview of the motivations. G.4 Structure of the Thesis
This thesis consists of seven chapters.
1. pp. Aug.1.
• L.
Other research contributions not presented in this dissertation are two conference papers:
• L.. De Natale. of ISSSTA 2008 Conf. Situation-aware radio
resource management for multi-rate MC-CDMA wireless networks targeted at mul-
timedia data exchanges in local areas. B. 2008. respectively. C. Granelli. De Natale. the present chapter.
Chapter 2 reviews the state of the art of multiple access interference avoidance/cancellation
techniques for CDMA systems and overviews channel estimation techniques particularly
used in multi-carrier and multiple antenna scenarios. March 2007. G. in Proc.
Chapter 1. Multicarrier CDMA for data trans-
mission over HF channels: application to digital divide reduction.
Chapter 3 and Chapter 4 introduce the proposed MUD techniques and consider the
use of evolutionary strategies to channel estimation purposes. of
Aerospace 2007 Conf. of ACM MOBIMEDIA 2006 Conf. in Proc.4 Structure of the Thesis 11
detection with GA-assisted channel estimation for STBC-MIMO MC-CDMA mobile
communication systems. 182187. and F. it contains outline and research contributions
of this dissertation. Sacchi. in Proc.. given their modular implementation. Sacchi.. the dealed prob-
lems and the proposed solutions. and F.
Sept. B. 2006. The outline of each chapter is as follows.

.

g. caused in CDMA systems by the cross-
correlation between spreading codes of active users) or the non-ideal channel eects (e. either using Single Input Single Output
(SISO) or MIMO systems. 2. in particular for multi-carrier and multiple antenna scenarios. The
second part of the chapter is dedicated to summarize the mostly used channel estimation
techniques. [26]). considering both Single. a review of multiple access interference avoidance/cancellation techniques for
CDMA systems is provided.1. In wireless communication channels.and Multiple-User approaches.g.
13
. an overview of various MAI reduction techniques will be provided
(following the classication reported in Fig.
2.
the intentional non-orthogonal signaling (e.Chapter 2
State of the Art
At rst.. It is well known that the capacity of a communication system is
strongly limited by Multi-Access Interference (MAI).
due to multipath propagation) can lead to lose the orthogonality between multiple users'
signals.
In this sub-section.1 Multi-Access Interference Mitigation Techniques for CDMA
systems
The trend to deal with more and more simultaneous users tends to allow users to transmit
simultaneously on the same frequency band..

14 State of the Art
Figure 2. 29]).1: Classication of various CDMA receiver techniques
2.. amplitude. The Maximal
Ratio Combining (MRC) detector consists of a lter matched to the channel's transfer
function.. 28]).. where MAI is treated as noise and no use of
information regarding the interference created by other users is done ([27]).1 Single-User Detection
The rst approach to deal with the MAI problem has been the simple use of the tra-
ditional Single-User Detection receivers. It is able to maximize the Signal-to-Noise Ratio (SNR) at the receiver output
by reducing the attenuation and eliminating the phase rotation induced by the multipath
reections ([20.g. This operation could further compromise the users' orthogonality.) is
often not possible. when an increase in the users' number occurs or when the
. spreading code.. and the Orthogonality Restoring
Combining (ORC) or Zero Forcing (ZF) detector attempts to eliminate the channel eect
by inverting its eects ([28.
In order to mitigate this behaviour.1.
These single-user techniques have been widely used in the second generation CDMA
system like IS-95 and are still implemented in mobile devices since knowledge of the pa-
rameters of the interfering users (e. However. phase. the Equal Gain Combining (EGC) detector corrects
only the phase rotation without handle the magnitude. position.

34. the Minimum Mean Square Error (MMSE) ([40.
.
In the linear multi-user detectors. Excellent reviews of them can be found in [33. numerous sub-optimal approaches with acceptable complexity have been pro-
posed in the literature.
Thus.
In general. 37. [32]). Therefore.
The optimum multi-user receiver has been presented by Verdu in [32]. users
equipped with single-user detectors may lose communication.2 Multi-User Detection
The natural evolution of the research has been to take into account all the available infor-
mation at the receiver in order to improve the CDMA receivers' performance by reducing
or cancelling the MAI. This phenomenon is known
as the near-far eect and it can be alleviated by using power control schemes ([30]). These methods are actually practical only at the base-station. 36.2. The resulting multi-user detectors are generally characterized by
a very high complexity but they oer a huge capacity improvement over the conventional
single-user detectors ([31]). 35.
2. they can be classied into two categories: linear and non-linear detectors.2.
where all the information is readily available and there are no power consumption con-
straints. the soft outputs of the conventional lters are linearly
transformed to reduce the access interference and provide better performance. The sequence of
symbols is selected by jointly considering the matched lter outputs of all users.1. 41])
and the Minimum Bit Error Rate (MBER) ([18]) detector.1 Multi-Access Interference Mitigation Techniques for CDMA systems 15
multiple users' signals are characterized by widely varying power level disparities. given that its complexity grows exponentially
with the number of active users (see Fig. It oers excellent performance gains over conventional systems but it
is too complicated for practical application.
The decorrelating detector linearly transforms the outputs of the conventional matched
lter receiver by applying the inverse of the correlation matrix of user spreading codes. The most
used are the decorrelating ([39]). 2. 38].
the resulting K -user Maximum-Likelihood (ML) sequence detector consists of a bank of
single-user matched lters followed by a Viterbi algorithm whose complexity per binary
( )
decision is O 2K .

In non-linear multi-user detectors (also called subtractive or interference cancellation
detectors).g.2: Conventional (a) and optimum (b) K -user detector
It has some interesting advantages over the conventional detector. the received sig-
nal power does not have to be estimated or controlled and the near-far eect is strongly
reduced ([37]). SIC) ([42. Therefore. the interfering signals are estimated and then removed from the received sig-
nal before detection. 16 State of the Art
(a) (b)
Figure 2. The disadvantages are the noise enhancement and the impossibility to
eliminate any Inter-Symbol Interference (ISI) caused by channel dynamics. 47. due to the correlation matrix inversion. PIC) ([44.. The interference cancellation can be carried out either successively
(Serial Interference Cancellation. 43]) or in parallel (Parallel Interference Can-
cellation. 45]).
Generally speaking. The MBER detector aims to
directly minimize the BER in output at the decoder. The compu-
tational complexity is O (K 3 ). e. 48]).
A deeper dive into the last two approaches will be taken in the follow of the dissertation. interference cancellers are much simpler than linear multi-user
detectors but are inferior in terms of performance ([46.
The MMSE detector applies a linear transformation to the output of the conventional
detector of a matched lter bank to minimize the square dierence between the transmit-
ted symbol sequence and the estimated symbol sequence. the core of this
thesis has been focused on the study and development of novel linear MUD techniques for
.

several modications have been proposed in the literature in order
to increase the performance and reduce the computational complexity.
The MMSE MUD techniques oer a polynomial complexity but are even able to easily
support adaptive implementations ([54]).
Thus. Such a situation has been considered in [52]. the basic MUD techniques for MC-CDMA are ML. it allows optimum performance even for full-loaded systems using large modula-
tions. It exploits the sparsity of the cross-correlation matrix
of CI codes to obtain a complexity that grows exponentially with the channel multipath
length instead of the number of active users. where a
low computational load ML MUD receiver based on the use of Carrier Interferometry (CI)
codes ([53]) has been presented. In particular.
The choice of these particular systems has been done in order to remain in the context
of the diversity-based 4G-wireless communication devices presented in the former Chapter
1. Two approaches based on the LMS and on the
. respectively. a Genetic Algorithm (GA) and a Neural Network (NN)
MUD receiver have been presented. MMSE and MBER detec-
tion. the application to MC-CDMA systems of detectors based
on the MMSE and MBER principles has been considered. but only for low noise received signals. It tends to destroy the orthogonality among users. Brunel used in [51] a low-complexity
optimum lattice decoder (the sphere decoder) to jointly detect all users.
Neither of the above techniques take into account the presence of Carrier Frequency O-
sets (CFOs). For each of them. In [49] and in [50]. both in the SISO and in the
MIMO scenario. greatly degrading the
performance of MC-CDMA systems. As previously
enounced. Its complexity is
a polynomial function of the number of users and is independent of the modulation size. MC-CDMA transmission techniques can jointly exploit the advantages of multi-carrier
OFDM and single-carrier Spread Spectrum DS-CDMA techniques ([15]).1 Multi-Access Interference Mitigation Techniques for CDMA systems 17
CDMA systems.
Unconventional mathematical tools have been recently used to implement near-optimum
ML MUD algorithms characterized by a reduced computational load with respect to the
Verdu's work [32] (polynomially growing with the number of active users instead of ex-
ponentially).2.

A modied version of the same adaptive algorithm is shown in [60]. where a supplementary GA-based MUD stage has
been inserted after a classical MMSE MUD stage. and the channel can be estimated
blindly by solving the minimum eigenvector of a data sub-matrix. proposed in [59] a LMS-style stochastic gradient adaptive algorithm based on the
approach of kernel density estimation for approximating the BER from training data. On the other hand.
The MBER criterion has been applied in several works to DS-CDMA systems. Chen
et al. It describes a Minimum Conditional BER (MCBER)
. 63.
A more reliable solution in the presence of uncertainties about channel estimation and
equalization has been described in [56]. it may always suer from the
same drawbacks of deterministic gradient based algorithms. driven by a blind channel identication
algorithm. A new pilot-based
channel estimation scheme has been used in [58] to drive a PIC stage with MMSE ltering. 18 State of the Art
RLS algorithm have been presented in [19] and another one based on the Normalized
Least Mean Square (NLMS) in [55].
A linear adaptive MBER MUD approach is proposed in [61] for asynchronous MC-CDMA
systems. A modied version of the same
algorithm is presented in [65]. Instead to use its estimation. 64]. It is based on the
Gradient-Newton algorithm and can speed up the convergence of the multi-user receiver. It
is based on the RLS updating of the correlation matrix. It employs an adaptive stochastic gradient algorithm based on the estimation
of kernel density function. consisting in the calculation of the receiver weights deriving from the
explicit solution of the MMSE equation ([15]) by using an estimation of the channel
matrix obtained through LMS/RLS methodologies. Such an approach can achieve better
performance than fully adaptive MMSE solutions as well as increased robustness to the
selection of the adaptation parameters. They are very ecient from a computational point
of view but their performances and convergence rates are strongly inuenced by the choice
of the LMS/RLS updating parameters. Another blind linear MMSE detector and channel estimator is shown in [57]. the true Probability Density
Function (PDF) is directly considered in [62. A possible solution to this behaviour has been
proposed in [19].
requiring shorter training data.

This typically happens
in the 3G WCDMA PHY-layer standard of Universal Mobile Telecommunications System
(UMTS) (based on DS-CDMA).
The key feature of MIMO techniques is to provide a potential increase of the channel
. multi-rate MC-CDMA performances could be substantially improved by the
adoption of multi-user detection.
The ow of multimedia trac through wireless networks is constantly increasing. the usual tradeo between data rate and robustness against channel eects
becomes more evident.1 Multi-Access Interference Mitigation Techniques for CDMA systems 19
receiver that minimizes the conditional probability of error for reducing the complexity
of the detector.
All the above techniques are referred to SISO scenario. In particular. ecient tech-
niques for multi-user and multi-rate data transmission should be investigated.
time. In a multi-rate context. just considering the diversity
in the frequency domain. In such a
perspective. In the case
of frequency-selective wireless channels and time-frequency diversity-based transmission
techniques.2. the diversity gain of higher-data rate
transmitters may be reduced with respect to lower-data rate ones. In this framework. and frequency). Multimedia trac is characterized by heterogeneous
data rates typical of the dierent streamed media.
Multi-rate transmission can be fairly managed also by 4G MC-CDMA schemes by at-
tributing to dierent user classes a dierent number of subcarriers and variable-length
orthogonal spreading codes [67]. Recent trends in R&D about mobile communication systems
are going towards the joint exploitation of diversity concept in dierent domains (space. The usual tradeo between diversity gain and data rate
turns on a lower number of subcarriers attributed to the fastest users that will be penal-
ized with respect to slowest users in terms of channel degradation and MAI. there is an increasing interest concerning the integra-
tion of MIMO techniques aimed at exploiting diversity in the space domain with digital
transmission techniques aimed at exploiting diversity in time and frequency. both
in local and in metropolitan areas. where variable spreading factors [66] are attributed to
various user classes in order to arrange multi-rate services ranging from 15 Kbps to 960
Kbps.

However. The columns of the matrix are transmitted in successive symbol periods. genetic algorithms based solutions [72]). Alamouti's scheme originally consid-
ered two transmit antennas and one receive antenna. Adaptive
antenna arrays have been already used in wireless communication systems since late 1960s. They are aimed at separating a desired signal
from interfering ones by controlling the radiation pattern of the array by adjusting the
array weights so that a certain optimization criterion is met. 20 State of the Art
capacity without an expansion of the Radio Frequency (RF) bandwidth ([68]). LMS and RLS algorithms
[71]. but
the upper and the lower symbols in a given column are sent simultaneously through the
rst and the second transmit antenna respectively. Alamouti demonstrated
that the same scheme can be easily generalized to two transmit antennas and a generic
number of receive antennas and successively Tarokh et al. Substantially Alamouti's coding is
an orthogonal ST block code.
This work was the starting point for intelligent or self-congured and highly ecient
systems: adaptive or smart antennas ([70]). where two successive symbols are encoded in an orthogonal
2×2 matrix. 76..
The conventional beamformers are based on the MMSE approach.
Applebaum's paper [69] presented a method for adaptively optimizing the SINR in the
presence of any type of noise environment (background noise and jamming signals).g.
The core idea in MIMO system is Space-Time (ST) signal processing in which the time
dimension is complemented with the spatial dimension inherent to the use of multiple
spatially-distributed antennas ([68. Commonly used ST coding schemes are ST
Trellis Codes (STTC) and STBC. A comparison between dierent MBER-based beamforming algorithms
is reported in [75]. computationally ecient and mathematically elegant
STBC scheme has been proposed by Alamouti in [78]. Several beamforming
algorithms based on it have been proposed in the literature (e. A solution based on this approach and on the use of genetic algorithms has been
proposed in [74]. described in [79] the possibility
to use even an arbitrary number of transmit antennas. Another usable approach is the MBER one
([73]). 77]).
.
An example of conceptually simple.

1 Multi-Access Interference Mitigation Techniques for CDMA systems 21
Closed form expressions for the average BER have been derived in [80] for STBC systems
with generic number of transmit and receive antennas. 87]. 86. where a GA-based MUD de-
tector and a turbo iterative receiver for the downlink of STBC MC-CDMA systems are
described. More advanced
techniques have been recently presented in [88] and [89]. by Bizzarri et al . respectively.2.
The MUD problem in STBC MC-CDMA systems has been already dealt.
The promising results yielded by MIMO and STBC techniques and the increasing
importance of spread-spectrum and multi-carrier modulations in next-generation cellular
systems design have recently suggested to researchers to protably combine diversity in
dierent domains. both over
Rayleigh and MIMO METRA ([84]) channels. a thorough overview of MIMO MC-CDMA implementations has been proposed
by Juntti et al. and.
In this last work.
Recent trends of R&D in diversity combining and MUD are moving from MMSE ap-
proaches to MBER approaches. STBC MIMO MC-CDMA systems have been regarded among the most
promising technologies for future cellular standards. in [82] for MIMO OFDM.
A combination of MMSE and MBER criterion has been presented in [76] to obtain a
multi-stage linear receiver for MIMO multiplexing systems. to provide
performances very close to the single-user bound. recent literature has shown that
BER cost function might be more suitable for MUD tasks than MSE one. The advan-
tages taken by MMSE MUD in MAI cancellation are well evidenced in [83]. In such a framework.
A rst adaptive MCBER detector for asynchronous DS-CDMA systems with multiple re-
ceive antennas is described in [90]. therefore. They can jointly exploit diversity in
dierent domains in order to counteract multipath fading eects and. in [16]. Its extension to general MIMO scenario is proposed in
. in [81] for MIMO DS-CDMA. eective schemes employing
blind or semi-blind channel estimation techniques have been discussed.
nally. m fading channels. Examples of space-time-frequency coding schemes have been shown by
Petre et al. In [85. transmitting data over MIMO
correlated Nakagami.

94]. It oers good
performance but its convergence is sensitive to the choice of the algorithm's parameters
(e. in particular
for those using multiple sub-bands and multiple antennas. decision-directed
and blind . which can be provided by a separate channel estimator.g.
The Space Division Multiple Access (SDMA) MIMO OFDM scenario has been considered
in [92] and [93. The latter uses a GA to over-
come these problems. its function is to
estimate the amplitude and phase shift caused by the multipath propagation for every
sub-band and for every transmit/receive antenna pair. For wireless
systems.
2. The MBER MUD's weight vectors are directly determined by the
GA with a lower complexity than the CG algorithm. according to the available information about the transmitted signal.
Recently.2 Channel Estimation Methods
All the previously presented detection techniques require the knowledge of the channel
impulse response.
An adaptive space-time equalisation ([68]) assisted MBER MUD scheme for Single Input
Multiple Output (SIMO) systems is described in [95] and its evolution using decision
feedback in [96.. In fact. An iterative version of the same approach is shown in [98]. The former presents a traditional adaptive Conjugate Gradient (CG)
algorithm for arriving at the minimum solution of the BER cost function.
. channel estimation can be dicult and computationally intensive. the initialization value and the step-size parameter). a joint generalized scheme of antenna combination (using a modied version of
the Antenna Subarray Formation (ASF) scheme presented by Karamalis et al.
The channel estimation techniques can be classied in pilot-assisted . The two
issues are jointly considered by solving the highly non-linear decision statistic through a
PSO algorithm. 97]. 22 State of the Art
[91] with the presentation of another receiver which aims to minimize the joint probability
of error for all users (MJBER). It is
particularly suitable in the over-loaded scenarios. in [99])
and symbol detection based on the MBER criterion has been proposed in [100].

the eectiveness of the linear interpolation in
terms of system complexity. the application of evolutionary strategies to channel estimation issues will be also
considered. 113.2. their placement. both for SISO and MIMO scenario. In the following sub-
sections. 110]. 114]) and the Fast Fourier Transform (FFT) inter-
polation ([115. more than
200 references are reported in [101]). given that estimation of the wireless channel is a very broad topic (e. considering that
two MMSE channel estimation techniques based on GA and PSO will be presented in this
thesis. 105. and their references. aecting not only the quality of CIR evaluation but the transmission rate as well. and cubic) ([111.
The importance of the pilot pattern choice has been evidenced in [102]. 116]). The
number and placement of pilots in the time-frequency grid has been extensively studied
in [103. A
novel linear interpolator for OFDM systems has been presented in [118] by Doukas and
Kalivas. The most important issues are the optimum choice of training
sequences. In [111] and [117].1 Pilot-Assisted
Pilot-assisted methods use a subset of the available subcarriers to transmit training se-
quences known to the receiver. processing delay and estimation accuracy has been shown. 112. 104.
In [119]. and of the channel parameters. their dimension and the used interpolation method.2. It represents an important
topic.2 Channel Estimation Methods 23
An extensive overview of channel estimation techniques employed in OFDM systems
has been presented in [101]. The desired frequency domain channel transfer function
is directly estimated over the pilots and an interpolation method is then used to obtain
the remaining values.g. both in time and frequency. a particular emphasis will be placed on the methods
developed for multi-carrier and multiple antenna systems.
2. Its performance in terms of MSE and BER have been expressed as a function of
the number and distance of the pilots. 109.
The most considered interpolation methods are the polynomial interpolation (linear. 106.
quadratic.. two channel estimation techniques for a MC-CDMA system transmitting over
. by comparing
in terms of BER several positioning of the pilot symbols. Furthermore. 108. 107.

proposed in [120] a three-step dedicated channel estimation procedure which
exploits all the existing pilot sequences as well as the structured dynamics of the channel
in WCDMA receivers. 24 State of the Art
a standard UMTS channel model are compared. The method starts with block-wise dedicated and common chan-
nel Least Squares (LS) estimates of the channels associated with dedicated and common
pilots.3 (a) and (b). it is further rened via
Kalman ltering by exploiting the channel temporal correlation.
Expectation Maximization (EM) based pilot-assisted channel transfer function estimation
. The other involves transmitting pilot and data symbols in separate
OFDM symbols and interpolating in time (see Fig.
MMSE-based frequency domain channel estimation is proposed in [122. [119]). 124] for
multiple-transmit antenna-assisted OFDM scenario.3: Arrangement of pilot symbols
Bastug et al. 2. 123. and. Afterward. respectively.
A robust pilot-based OFDM channel estimator based on the combination of low-pass l-
tering and delay-subspace projection has been presented in [121]. nally.
(a) (b)
Figure 2. the initial LS estimates are optimally combined to obtain an improved
unbiased MMSE estimate of the dedicated channel. One considers transmitting pilot and
data symbols in the same OFDM symbol and performing interpolation in frequency be-
tween pilot symbols.

Regarding the space-time coded OFDM scenario.2 Decision-Directed
The Decision-Directed (DD) approaches consider all the subcarriers as pilots.2. and therafter the newly detected data is used for the estimation of the current
channel. The resulting channel transfer function can be accurate in the absence of symbol
errors and in particular for slowly varying fading channels.
A similar subtraction-based approach has been applied in the time domain in [133].
In [128].Minimization (Maximization) (MM)
algorithm has been presented in [127]. Then channel
gains are estimated by ML estimator.2. The channel
estimation of a previous OFDM symbol is used for the data detection of the current
estimation. For each specic receive antenna. a PSO-assisted frequency osets and channel gains estimation algorithm for
MIMO systems is described.
GAs are used in [129] to estimate low Peak-to-Average Power Ratio (PAPR) near-
optimum training sequences for channel estimation in an OFDM system. re-
sulting in a sort of frequency domain PIC-assisted DD channel estimation.2 Channel Estimation Methods 25
approaches have been shown in [125] and [126] for MIMO OFDM systems. It exploits the independence of the transmitted
subcarrier symbol sequences to recover the dierent transmit antennas' channel transfer
functions. considered in [132] a subtraction-based channel estimator for the case with
two transmit and two receive antennas. It is shown that
such training sequences are able to improve channel estimation and BER performance in a
coherent system with a large number of sub-channels.
Jeon et al. a least-squares error channel estima-
tor has been presented by Li et al.
2. the interfering
signal contributions associated with the remaining transmit antennas are subtracted. in [131]. Their gener-
alization in the context of the Majorize (Minorize) . The evolutionary strategy is used to obtain a rst estimate
of the frequency osets (assuming that a training sequence is available). A similar work has been presented
in [130] in the context of multiple antenna aided systems. without signaling overhead. with
.

26 State of the Art
a further deepening in [134]. properties of the transmitted
signal such as constant magnitude. alternatively.
2. showing the superiority of the latter over the
former.
A robust and ecient EM-based estimation algorithm is considered in [137]. respectively. proposed in [145] a blind channel identication and equalization al-
gorithm for OFDM-based MIMO systems using non-redundant antenna precoding and
second-order cyclostationary statistics. It is shown to be practical in
the context of transmitting consecutive training blocks. 140] and [141.
Wiener and Kalman channel estimation and tracking algorithms have been described in
[139. 142].
A novel channel estimation method which optimally combines the decision-directed
and the previously described pilot-assisted framework has been shown in [144]. It separates the superimposed received signals
into their signal components and estimates the channel parameters of each signal compo-
nent separately. Another EM-based method has been described in
[138] for MIMO STBC OFDM systems. It partitions
the problem of estimating a multi-input channel into independent channel estimations
for each transmit-receive antenna pair.
Gong and Letaief described in [136] a MMSE-assisted DD channel estimator as an exten-
sion of the least-squares based approach presented in [131].2.3 Blind
Blind methods do not require any training sequence to be transmitted but exploit the
spatial or temporal structure of the channel or. Non-redundant linear block precoding has been
used extensively in such a context ([146]).
It returns to consider the channel's correlation in the frequency direction instead of that
in the time direction followed in the works of Li. It can be considered as a simplication of [131]. Another
reduced-complexity version of the method proposed in [131] has been described in [135].
Bolcskei et al.
. A comparison of LMS and RLS channel estima-
tion techniques has been provided in [143].

it exploits the signal space diversity
information and the signal time diversity information. 161.2 Channel Estimation Methods 27
In [147].
Coherent processing across the subcarriers has been used in [155] to estimate the channel
in the time domain. It estimates
the channel parameters by computing the eigenvectors of a matrix containing 4th-order
cumulant matrices of the observations. 160. Some solutions to this so-called multi-dimensional ambi-
guity ([150]) have been proposed in [151. 153.
It uses particle swarm algorithm to estimate the outputs of channel equalizer. They can not be directly applied in MIMO-OFDM
scenario if the number of antennas at the receiver is smaller than or equal to the number
of antennas at the transmitter.
A modied PSO algorithm based on Lotka-Volterra competition equation has been pro-
posed in [169]. a method based on Singular Value Decomposition (SVD) accompanied by a
simple block precoding scheme is described. 152.g. [158. 165. 159. 166.
A blind strategy addressed to the 2×1 Alamouti system is described in [156]. [163.
Several other techniques have been recently published in the context of STBC systems. It is applied to determine the impulse response of a SIMO system in a
. It
holds the advantages of the previously described scheme without sacricing the coding
rate for any number of transmitting antennas (provided that the numbers of transmitting
and receiving antennas are equal). but the coding rate decreases with the number of transmit-
ting antennas.
167]). A similar work was presented in [157]. It estimates the channel parameters in the time domain jointly for
all subcarriers instead of doing this in the frequency domain independently for each sub-
carrier.
A blind PSO-based MIMO channel identication technique has been presented in [168]. 162]) and MC-CDMA (e. 164. and put the
estimation to the inverse lter algorithm. 154].g...2. Therefore.
The traditional subspace based methods have been developed in [149] in the general
context of digital communications.
both OFDM (e. Its advantage is that it does not require a
decoder at the receiver side. An improved version of the same method has been presented in [148].

In such a
situation.
Joint blind channel estimation and symbol detection schemes using GAs are described
in [170] and [171]. the limitations of conventional detection and channel estimation techniques are
well known ([175]).
In the context of SDMA multi-user MIMO OFDM scenario.
. The former is a single-user receiver divided into a two-layer optimization
loop: the unknown channel model is obtained through a micro genetic algorithm (µGA. Jiang et al. 28 State of the Art
blind way and outperforms the original PSO.
The latter is always based on the ML decision rule and proposes a multi-user detector
that jointly performs the channel estimation and the symbol detection using the same
GAs simultaneously.
[172]) and then a set of Viterbi algorithms (one for each member of the channel population)
is used to provide the maximum likelihood sequence estimation of the transmitted data
sequence. 174] a GA-assisted iterative joint channel estimation and multi-user detection
approach. proposed
in [173. It is able to provide a robust performance when the number of users is higher
than the number of receiving antennas (the so-called overloaded scenario).

Chapter 3
Novel Multi-User Detection Techniques
for Multi-Carrier CDMA Systems
This Chapter is aimed at describing some novel Multi-User Detection techniques partic-
ularly suited for multi-carrier CDMA scenarios from an algorithmic point of view. a GA-assisted MUD approach is described for semi-adaptive per-carrier
MC-CDMA systems transmitting over time-varying multipath fading channels. Both are developed for single-antenna MC-CDMA systems.
the description of the MC-CDMA received signal model is provided. Fi-
nally. Then. Some
common guidelines followed for their implementation will be provided in Chapter 5. the description of two MUD techniques based on the MBER approach will be
provided for the multi-antenna STBC MC-CDMA context.
At rst. At rst. a GA-assisted Maximum-Likelihood MUD receiver for multi-rate MC-CDMA sys-
tems will be proposed. the GA-
assisted MMSE MUD algorithm is detailed.
Then.
29
.1 A Genetic Algorithm-Based Semi-Adaptive MMSE Receiver
for MC-CDMA Mobile Transmission Systems
In this section.
3. a GA-assisted MUD approach based on the MMSE criterion is described.

hn (t) is a complex Gaussian process
with Rayleigh-distributed envelope and uniformly-distributed phase.
low-pass ltered by a bank of integrators.3)
i=−∞ n=0 k=1
where hn (t) is the complex time-varying channel coecient related to subcarrier n. with zero mean and variance
ση2 . 3. the received MC-CDMA signal can be expressed as follows:
∑ ∑
+∞ N −1 ∑
K
2πnt
y (t) = hn (t) ckn aki ej ( T ) p (t − iT ) + η (t) (3.1 A Genetic Algorithm-Based Semi-Adaptive MMSE Receiver for MC-CDMA Mobile
Transmission Systems 31
• N is the number of subcarriers
• K is the number of active users
• ckn represents the n-th chip of the spreading sequence of the k -th user (Hadamard-
Walsh sequences of length N have been employed)
• aki is the message symbol transmitted by the k -th user during the i-th signalling
period of duration T
• p (t) is a Non-Return-to-Zero (NRZ) rectangular signalling pulse shifted in time given
by:


1 for 0 ≤ t ≤ T
p (t) . T À Tm ) and
the signal amplitude A is equal to 1.
The received signal is then processed by a coherent FFT-based OFDM demux block. The samples of η (t)
are independent and identically-distributed in Gaussian way. and nally sampled at sampling rate equal to
.1 is transmitted over a Rayleigh multipath-fading chan-
nel.
The signal expressed in Eq. aki ∈
{−1.e. so that each transmitted symbol corresponds to a single bit.2)

0 otherwise.e...3. The transmission data
rate is chosen in order to assure at fading over each single subcarrier (i. and
η (t) is the Additive White Gaussian Noise (AWGN). characterized by a delay spread Tm and a Doppler spread Bd .
Let's suppose to employ a Binary Phase Shift Keying (BPSK) modulation (i. (3. +1}).
Under such hypothesis.

3. the real h (i)
could be replaced by its estimation ĥ (i). 19]):
h∗n (i)
wnOpt.2 The Proposed GA-Assisted MMSE MUD Receiver Structure
The choice of the diversity-combining weights can be done according to the various tech-
niques proposed in the previous Chapter 2 (i. 3. . . .7)
|h∗n (i)|2 + 2ση2/KEc
∗
where Ec is the energy per chip and the superscript operator is the complex conjugate
operator.7 requires the exact knowledge of the
N -element channel vector h (i) = [h0 (i) . (i) = (3. The resulting decision variable is given
by:
∑
N −1
k
λ (i) = wn (i) yn (i) (3. a
per-carrier MMSE multi-user detector has been considered in this part of the work ([19]). . . The sample obtained from the subcarrier n during the i-th signalling interval is given
by:
∑
K
yn (i) = hn (i) ckn aki + ηn (i) (3.6 is the following ([15.
The corresponding optimization criterion is to nd an N -element weight vector wOpt.. If it is not available. EGC. wN −1 (i) that minimizes the mean square error between the equalized re-
ceived signal and the noise-less pattern we would have on each subcarrier at the receiver. obtained by using one of the channel estimation
.). ORC. The explicit solution to
Eq.4)
k=1
The received signal energy scattered in the frequency domain is then combined by mul-
tiplying each sample yn (i) by a specic gain. MRC.e. .
w0 (i) . (i) =
[ ]
Opt.6)
wn (i) ¯ ¯
k=1
where E {·} represents the mathematical expectation operator.
Such a problem can be formalized as follows:
¯( ) ¯2 
¯ ∑K ¯
¯ ¯
wn (i) = arg min E ¯
Opt.1. . In particular. 32 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
1/T ..
The use of the ideal weights expressed in Eq.. . Opt.. hN −1 (i)].5)
n=0
3.
cn ai − wn (i) yn (i)¯
k k
(3.

the stochastic values assumed by
the channel coecients acting over each subcarrier are strongly correlated.
The GA works with a selected parameterisation in terms of generation number GT r .8 by the sample average made on the entire
duration of the training sequence.2 (b)).
In particular. 3. The GA convergence would be seriously aected
by noise eects (particularly at low SNR) if such an average operation would not
be performed. There-
fore. This approximation is correct if the channel can be modeled as an
ergodic stochastic process.2 (a)).
This training step is repeated with a period approximately equal to the coherence
time of the channel. crossover and mutation probabilities αT r and γT r . working with a dierent parameterisation and a dierent tness function.
The updating procedure is carried on symbol after symbol and it is initialised by
.e.8)
B i=1 ¯ k=1 n i ¯
The GA-based computation of the optimal weights is performed after having buered
B samples of the received signal yn (i) (see Fig.
population size PT r . the time variations of the channel impulse response are reasonably small and
a decision-directed updating step can proceed (see Fig. It can be shown that the GA-based estimation of the
MMSE weights is unbiased if the following relation is valid for each subcarrier:
(K )2
1 ∑B ∑
ckn ãki =K (3.. 3.9)
B i=1 k=1
It happens when the training sequences of the users are orthogonal. some limitations are imposed to the system.
2. The ensamble average of
Eq. 3. Decision-directed step: during a coherence period. It is performed by
the GA. If they are ob-
tained from Hadamard-Walsh matrices. The task of GA is to compute for each
subcarrier the weight ŵnT r (i) that minimizes the following metric:
¯( ) ¯2
1 ∑ ¯¯ ∑ k k ¯
B K
¯
Λ (ŵn (i)) = ¯ c ã − ŵ n (i) y n (i)¯ (3. respectively. B > K ).6 has been replaced in Eq. 34 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
of B bits is transmitted for each user k. 3. the training sequences' length has to be equal to a power of two and
greater than the number of users (i.

Experimental
trials pointed out that a good choice for this parameter is:
{¯ ¯}
σup ' max ¯wnOpt. The B -bit known training sequence is trans-
mitted.11)
¯ ¯
k=1
Given that only one generation runs. The new population is
stochastically generated in Gaussian way starting from the solution computed at the
previous signalling period ŵDD (i − 1) and imposing to the Gaussian generator an
updating standard deviation σup . respectively). but it is reasonable because only small variations
of the channel amplitude and phase are to be tracked during the coherence period. An explicit mathematical link is very dicult to be ob-
tained. the values of crossover and mutation probabilities are αDD = 0
and γDD = 0. .n
Among the new population.10)
i.. the eect of possible symbol errors on weight estimation
are conveniently reduced.e.e. it is chosen the individual that minimizes the following
metric: ¯( ) ¯2
¯ ∑K ¯
¯ ¯
Ω (ŵn (i)) = ¯ ckn âki − ŵn (i) yn (i)¯ (3.
ii) The Training-aided step begins.
This updating procedure is light. It should be increased as Doppler spread and SNR increase. During each
symbol period.
Moreover.3. The generation number GDD is equal to one and the
population size PDD has to be chosen. B samples of the received signal are stored.. It can be summarized as follows:
i) At time t=0 the GA-based procedure is initialised by a constant-value popu-
lation ŵ (0) = [1. and the weights vector ŵT r
.
These two steps are opportunely combined to obtain the whole GA-based MMSE MUD
procedure. (i − 1)¯ (3. crossover and mutation operators do not work
in such a step (i.1 A Genetic Algorithm-Based Semi-Adaptive MMSE Receiver for MC-CDMA Mobile
Transmission Systems 35
the solution computed during the training-aided step (i. in such a way. ŵT r (i)). The estimated data symbol âki is used to
calculate the tness function Ω (ŵn (i)). . 1]. (i) − wnOpt. Such a system parameter is linked to the Doppler
spread and to the SNR. a single generation of individuals is produced. . .

3. A xed amount of bandwidth is allocated for downlink
transmission. The population size PDD has to be chosen. The GA
parameters (GT r . Then.
3.2.
iii) The Training-aided step ends with the computation of the receiver weights at
the time t = BT + εT (ε is the execution time of the GA-based optimisation
procedure expressed in number of bit periods). γT r ) are opportunely chosen. 36 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
is computed by minimizing the per-carrier cost function of Eq. and γDD = 0.2 Genetic Algorithm-Assisted Maximum-Likelihood Multi-User
Detection for Multi-Rate MC-CDMA Systems
In this section. At rst. The transmitting
. to maintain the computational burden of a polynomial order with respect to
system parameters. the description of the multi-rate Variable-Spreading-Length
(VSL) MC-CDMA received signal model is provided. αT r .8. the GA-assisted ML MUD
algorithm is detailed.
where Wcoh is the coherence time-window of the channel. Now. 3. The GA is re-initialised
with the weights computed at the end of the coherence time-window and re-
parameterised in order to start again with the Training-aided step of ii). reached by spending a reasonable computational eort. PT r . The claimed objective is.1 Multi-Rate VSL MC-CDMA Transmission
Let us consider the multi-rate multi-user MC-CDMA transmission concept illustrated
in [67] known as VSL access. the application of GAs to multi-rate MC-CDMA multi-user detection
is proposed and discussed in order to obtain a near-optimum solution to the ML MUD
problem. A xed number of subcarriers N (being N an integer power of 2 in order to
simplify the FFT implementation of the VSL transceiver) is available.
in particular.
iv) The Decision-directed updating step ends at the time t = BT + εT + Wcoh T . αDD = 0. the GA switches to the
Decision-directed modality and its parameters are reassigned as follows: GDD =
1.

e. The decomposition in
frequency domain. class M ). . M bitrate classes can be
dened by the rule: rm = 2(M −m) · r (with m = 1. According to [67]
and in order to simplify the mathematical formalization of the received signal.i c(m) exp 2π (i − 1) +n (3. A block diagram of the
corresponding transmitter scheme is depicted in Fig. 3.2 Genetic Algorithm-Assisted Maximum-Likelihood Multi-User Detection for Multi-Rate
MC-CDMA Systems 37
user population is subdivided into user classes.
Figure 3. cu. is given
as follows:
∑
m N∑
m −1 {[ ] }
(m) N t
x(m) (t) = A(m)
u Du. . it is possible
to regard a user with rate rm as 2(M −m) eective users at rate r.4.
. namely xu (t) . .12)
u
i=1 n=0
u. also named basic data-rate of the system.n
2(M −m) T
(m) (m)
where 0 ≤ t ≤ T . 3. where r = 1/T is the bitrate
of the slowest user class (i.. The
data stream of a user of class m is converted into m parallel outputs. .n is the n-th chip of the
(m)
assigned spreading code. each one transmitting a digital data
stream over a subset of Nm subcarriers. Symbols at each
output are copied over Nm = N/2(M −m) branches and then respectively being multiplied
by the corresponding bit of spreading codes whose length is Nm .i is the i-th binary BPSK data symbol. More in details.3 ([67]). M ). and Au is the user's u transmitted amplitude. Du.3. whose cardinality is an integer fractional value
of N and depends on the channel bitrate rm .3: Illustration of VSL accessed multi-rate MC-CDMA
(m)
The baseband transmitted signal of the u-th user with rate rm . corresponding spreading codes and spectrum allocation for eective
users are pictorially described in Fig.

completed by zeros in correspondence of spreading codes assigned to users transmitting
at higher rates.
In the proposed approach.
In Fig. as dened in [67]. 3.3. So. The eective users'
number increases with: a) the number of user classes M .2. This implies that symbols coming from fastest users involve a reduced
amount of MAI. Also.16)
D
H
where the superscript operator denotes the Hermitian transpose.
3. the owchart of the proposed GA-assisted ML MUD detection algorithm
is depicted. Under such hypothesis. The channel matrix
H is supposed here to be completely known. and therefore
Ψ. and b) the number of orthogonal
subcarriers N. with respect to the symbols transmitted
by the eective users D̂.
= arg min Y − HΨĪD Y − HΨĪD (3. In VLS access. Fig.2 The Proposed GA-Based Multi-User Detection for Multi-Rate VSL
MC-CDMA
The optimal MUD in multi-rate MC-CDMA VSL systems is based on the ML criterion
([67]). it has been choose to select other individuals as those ones
.2 Genetic Algorithm-Assisted Maximum-Likelihood Multi-User Detection for Multi-Rate
MC-CDMA Systems 39
of the eective user.16 is regarded as the tness of the GA. The initial population consists of Ppop individuals obtained starting from
the solution D̂0 obtained by the hard decision made at the output of a single-user EGC
receiver stage. In particular. the absolute squared error between the received signal and the
reconstructed noise-less pattern. are assumed to be known by the receiver. the metric of Eq. the ML-based
computation of D̂Opt. that is:
{( )H ( )}
D̂ Opt. 3.5. the computational burden of theoretical ML detection in
the multi-rate case can reach huge amounts that might not be supported by commercial
signal processing hardware products. the OVSF code matrix. is theoretically feasible. the matrix Ψ contains elements
of the Orthogonal Variable Spreading Factor (OVSF) sequence matrix described in [67].4 clearly evidences that the number of eective users is higher than
the number of real users. The price to be paid is a computational
load exponentially growing with the number of the eective users U. ML MUD is implemented by minimizing. 3.

Such a criterion has been considered in order to
provide a good choice of the initial population without spending a relevant computational
eort. Such a choice might be not very suitable
in the presence of high level of MAI. results shown in
[15] that evidenced a worst behaviour of MRC with respect to EGC in case of increasing
level of MAI). the initial population is generated by a stochastic mutation of the hard
decision made on the output of a MRC stage. The initialization strategy proposed in this work is very similar to the
. it is known by literature that MRC is
the optimal combining methodology in the single-user case. 40 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
Figure 3. In fact.5: Flowchart of the proposed GA-assisted ML MUD algorithm
that dier from D̂0 by a Hamming distance lower or equal to a maximum given value
dhamm . The number of the individuals belonging to the so-generated population can
∑dhamm ( U )
be obtained as: Ppop = p=0 P
.g. e.. In [49]. but its performances rapidly
deteriorate when the number of simultaneous users increases (see.

After the initialization. But.
in which an individual is created by randomly choosing the i-th bit of the new generated
solution among the i-th bit present in one of the two selected parents. Crossover is applied on solutions belonging to the search space with
an assigned probability α. EGC combining is
very simple in case of perfect knowledge of the channel matrix and would not signicantly
aect the computational burden of the entire receiver chain. The diversity gain. The crossover strategy adopted here is the uniform crossover. The exploitation of the full potential of STBC MC-CDMA techniques can be
.16. is obtained at the price of an increased system
complexity.3. At
each generation. 3. The mutation
operator is applied by change a bit of the selected individual with an assigned probability
γ. Such a last
choice may theoretically perform better than the one here proposed. increased
with respect to the conventional SISO case. elitism has been used in order to maintain the best individual from the
generation j to the next generation j + 1. The population generation terminates when
a satisfactory solution has been produced or when a xed number of iterations Jgen has
been completed. where the initial population was generated among the elements
diering by dhamm from an initial solution obtained by a MMSE MUD stage.3 An Adaptive MBER Receiver for Space-Time Block-Coded MIMO MC-CDMA Mobile
Communication Systems 41
one considered in [56]. Furthermore.3 An Adaptive MBER Receiver for Space-Time Block-Coded
MIMO MC-CDMA Mobile Communication Systems
The potential advantages of implementing linear MBER MUD in STBC MC-CDMA con-
text can be easily explained.
Selection is performed by analyzing two individuals and choosing the one with the best
value of the tness. the MMSE MUD implementation would involve the inversion of a big
( )
N ×N matrix HΨĪΨH HH + N0 I that might be not a trivial task. a
tness value is associated to the Ppop individuals by computing the metric of Eq. STBC MC-CDMA is a transmission methodology where
diversity is obtained both in space and in frequency domain. from a more
practical viewpoint.
3. GA stochastic operators are applied in order to evolve the population.

. 2. . the space-time block coding technique proposed by Alamouti in [78] has
been adopted.
At rst.
Let us denote with K the number of mobile users and with N the number of orthogonal
subcarriers. Then. .
.6). a synchronous multi-user STBC MC-CDMA system equipped with NT x = 2
transmit antennas and NRx = 2 receive antennas has been considered (see Fig.
the LMS-based MBER MUD algorithm is detailed.. . periodically aided by a training sequence
and working in decision-directed modality during a coherence time window of the channel. From the
transmitter side.. and k = 1. ak (i) and ak (i + 1)) are mapped to the variables ak1 and ak2 . The application of MBER reception to MIMO MC-CDMA is not straightforward
and has to be investigated by carefully taking into account tight requirementes in terms
of ease of implementation and reduced computational eort.
3.. .1 System Description
In this work. two consecutive symbols
of the generic user k (i. K . Let {ak (i)}i=0. 3. the description of the STBC MIMO MC-CDMA system is provided.e. the investigation of MBER strategies can be regarded as a step-ahead towards the
computationally-aordable signal detection optimization also in the STBC MC-CDMA
case. Therefore. The practical implementation of the proposed MBER
receiver relies on an adaptive LMS algorithm.. 42 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
obtained only by means of optimized multi-user detection approaches. In such a perspec-
tive. ak (i) and
ak (i + 1)) are mapped to the transmit antennas according to the code matrix given by:
   
1 ak (i) −ak (i + 1)∗ ak1 −ak∗
Φk (i) = √   = √1  2
 (3. two consecutive symbols of the generic user k (i. . Without loss of generality..
The presented algorithm relies on the estimation of the probability density function of
the decision variables obtained by using the Parzen's windows methodology. .3. be the original information sequence of user k.
This section describes an adaptive multi-user receiver based on a Minimum-BER ap-
proach for synchronous STBC MIMO MC-CDMA systems transmitting data over time-
varying multipath fading channels.17)
2 ak (i + 1) ∗ 2 ak
ak (i) 2 ak∗
1
with i = 0.1. 2.e.

This mapping will be useful in order to make more readable the mathematical
formulation exposed in the following. the received signal can be conveniently written
in this form:
       
yj (i) H1j H2j C 0 a (i) nj (i)
 =   +  (3. . hu. . .N ) where hu.3 An Adaptive MBER Receiver for Space-Time Block-Coded MIMO MC-CDMA Mobile
Communication Systems 43
Figure 3.n (u ∈ {1. The two columns of Φk (i) are transmitted during
two consecutive time slots (i and i + 1).j. Let us suppose that
.)
T
([·] denotes the matrix transposition)
• Huj is the diagonal channel matrix diag (hu.j. yj. 2}) during the i-th signaling period (i = 0.3. 2})
is the channel coecient associated with the path associated to the u-th transmit
antenna and to the j -th receive antenna over the n-th subcarrier.N (i)] is the vector of received signals related to the j -th
receive antenna element (j ∈ {1. .j. . . The rst element of each matrix column is sent
by the rst transmit antenna and the second element by the second antenna.1 (i) . .
Adopting a more agile matrix notation.6: The considered STBC MC-CDMA system (2x2)
respectively. 2. . respectively. . . .18)
yj∗ (i + 1) H∗2j −H∗1j 0 C a (i + 1) n∗j (i + 1)
where:
T
• yj (i) = [yj.1 .

the known training sequence is
replaced by the decision made on the received symbol at the previous LMS updating step.
A block scheme of the considered STBC MC-CDMA transmission system is drawn in Fig. a1k and
. The
decision-directed updating procedure starts at the end of the training phase and goes on
till the end of the coherence time window. the description of the considered system model is provided. .4 A Linear Multi-User Detector for STBC MIMO MC-CDMA Systems Based on the Adaptive
Implementation of the Minimum-Conditional BER Criterion 49
convergence ([178]). The MCBER combiner has been implemented in adaptive way
by using LMS optimization. a synchronous multi-user MIMO MC-CDMA system based on
Alamouti's Space-Time Block Coding ([78]) has been considered. a linear multi-user detector is described for MIMO MC-CDMA systems
with Alamouti's space-time block coding.3.
Two consecutive data symbols of the generic user k (k = 0.e. a
short.
3. K − 1) (i. known.
3. . training sequence is periodically transmitted in order to provide a faster
convergence of the LMS procedure to the wanted solution. In this case. Practically.
3. The extension to scenarios charac-
terized by an increased number of transmitting and receiving antennas is straightforward. Then.
At rst. In order to solve this problem and to speed up the convergence of
the algorithm. Two transmitting an-
tennas and a single receiving antenna are employed.4 A Linear Multi-User Detector for STBC MIMO MC-CDMA
Systems Based on the Adaptive Implementation of the Minimum-
Conditional BER Criterion
In this section.1 System Model
In the present dealing. .4.. The period of transmission
of the training sequence approximately equals the coherence time of the channel.7. a training-assisted adaptive procedure has been adopted. inspired by the concept of Minimum Conditional
Bit Error Rate (MCBER). the theo-
retical MCBER MUD criterion is explained. .

The classical antenna diversity approach considers the utilization of
multiple antennas at the receiver side and a single antenna at the transmitter side.e. As
result. Essentially.7: The considered STBC MC-CDMA system (2x1)
a2k ) are mapped to two transmitting antennas according to the code matrix Φk . This is the conguration considered in this part of the work.
.
The most economic and advantageous Alamouti's congurations consider two transmit
antennas (installed at base station or access point) and a single antenna mounted at the
receiver side.35)
2 a2 a1∗
k k
This matrix represents the Alamouti's STBC block. where the
focus is on the development of cost-eective mobile terminals. whose
elements are given by:
 
1 a1k −a2∗
Φk = √  
k
(3. Alamouti's scheme exhibits some
clear advantages. 50 Novel Multi-User Detection Techniques for Multi-Carrier CDMA Systems
Figure 3.
The encoder outputs are transmitted during two consecutive transmission periods (i. Alamouti's STBC scheme makes available a space diversity gain also
for mobile terminals (therefore in the downlink) only by exploiting transmit diversity.. the receiver becomes larger and more expensive. This is the reason why since
many years antenna diversity has been exploited only by base stations in the uplink
([78]).

1. the application of Genetic Algorithms and Particle Swarm Opti-
mization techniques will be considered in the context of multi-carrier and multi-antenna
systems.Chapter 4
Application of Evolutionary Strategies
to Channel Estimation in MIMO
Multi-Carrier Scenarios
This Chapter takes into account the use of evolutionary strategies to channel estimation
purposes.and multi-user cases.
4.1 Genetic Algorithm-Assisted Channel Estimation for STBC
MIMO MC-CDMA Systems
The next sub-section is aimed at investigating the terminology and the basic functioning
idea of GAs. a description of the GA-assisted channel estimation techniques will
be provided.1 Basics of GAs
Genetic algorithms have a 20-years history of successful applications in telecommunica-
57
.
4. Then. In particular. both in the single.

58 Application of Evolutionary Strategies to Channel Est. because a new population of individuals is computed at each new generation.
A more detailed description can be found in [21. the genetic operators of crossover and mutation
are applied on selected chromosomes with probability α and γ respectively. In particular. in MIMO Multi-Carrier Scenarios
tions.1. As described in Chapter 2. 23.
In such a context. the convergence to the optimal solution is theoretically guaranteed (provided that
a proper parameterisation of the GA procedure is set).
Standard GA implementations represent feasible solutions as a set of individuals (called
population ).1. HB ) = °Y − ĤA CA − ĤB CA ° +
1
° ( )∗ ( )∗ °
° °2
+ °Y2 + ĤA C A2 − ĤB C A1 ° (4. the GA-based procedure can dynamically adapt itself to time-varying system condi-
tions. the MMSE approach proposed in [182] has been modied for
the STBC MIMO MC-CDMA system with Alamouti's coding previously described in
sub-section 3. the target of the GA is to minimize with respect to the estimated
channel matrices the following MSE metric:
° °2
° 1 2°
J (HA . 22. The population generation
terminates when a satisfactory solution has been produced or when a xed number of
generations has been completed. useful to
solve complex problems with reasonable computational eort ([21]):
1. 24].
At each iteration (namely: generation ).1)
This is not a trivial task from a computational point of view.2 The Proposed GA-Assisted Channel Estimation
In this sub-section. avoiding that solution be
trapped in local minima.
2.
4. The cost function to be minimized (or maximized) is called tness function .4. signal processing and electromagnetic elds due to some basic features. in order
to generate new solutions belonging to the search space. an MMSE channel estimation targeted to MIMO STBC systems is
considered.
state of the art methodologies for channel estimation in STBC systems are substantially
.

During a coherence time period. growing more and more
with the users' number K. . It follows a strategy similar to the one described
in section 3.2 for a detailed description). . a GA-assisted
MMSE strategy has been implemented. . population size PT r . . . K ∧ j = 1. . . Training-aided step: during this step. The GA optimizer
computes at each signaling period the estimated channel matrices using a selected
parameterisation in terms of generation number GT r . . the vectors of known bits Ă1 = ă2j−1 k : k = 1. In such a
{ }
way. K ∧ j = 1.
2. L/2 and
{ }
k : k = 1. In order to avoid such kind of operations. and others are strongly
inuenced by the choice of the distinguishing parameters. The bits of
the training sequence are organized in L/2 consecutive pairs. . . .1 Genetic Algorithm-Assisted Channel Estimation for STBC MIMO MC-CDMA Systems 59
based on the insertion of training sequences and on the inversion of signal covariance ma-
trices. L/2 are employed to compute the MSE met-
Ă2 = ă2j
ric (j is the index of the signaling period). several techniques
have been proposed in the literature (see section 2. . crossover
and mutation probabilities αT r and γT r . Decision-directed adaptive step: the outputs of the training step are the channel
( )T r ( )T r
matrices ĤA and ĤB obtained by a GA-based optimizer parameterised in
such a way to learn the channel in reliable way. some are conceptually complex and computationally hard. . . the
stochastic values of the channel coecients acting over each subcarrier are strongly
correlated. an L bit-length binary training sequence
[ ]
ăk = ă1k . . . The training step is repeated with a
period approximately equal to the coherence time of the channel.4. . By this. ăLk is transmitted in form of header for each user k.
In order to obtain an adaptive and robust channel estimation technique. a decision-directed adaptive updating step should be reasonably
. How-
ever. It is articulated
into two steps:
1. . The footer Tr means that the
GA parameterisation is related to the training step. . . each one corresponding
to a pair of symbols transmitted in two consecutive signaling periods.1 for the linear MUD in the single-carrier MC-CDMA case. The application of such methodologies to the case of multi-user STBC MC-CDMA
systems would require the inversion of big covariance matrices. respectively.

in MIMO Multi-Carrier Scenarios
forecast. The GA parameters (GT r . the decision-directed updating step is performed by
the GA. The GA-based updating pro-
cedure is initialized by the solution computed during the training-aided step. . the identity matrices have been chosen for initialization.1. the whole GA-assisted channel estimation
procedure can be summarized as follows:
i) At time t=0 the GA-based procedure is initialized by a constant-value popu-
lation. K}. αT r . . In such a step. 4. in such a way. because only a GA generation runs.
the eects of possible symbol errors on channel estimation are conveniently reduced. In the present dealing. .
( )T r ( )T r
ĤA and ĤB . The L-bit known training sequence is transmit-
ted and the estimated channel matrices are computed by minimizing the cost
function of Eq. i.
iv) At the beginning of the adaptive step. . In particular.e. K}
and Ã2 (j) = {ã2k (j − 1) : k = 1. ..
iii) The Training-aided step ends with the computation of the channel matrices at
the time t = LT + εT (ε is the execution time of the GA-based optimization
procedure expressed in number of bit periods T ). During a symbol period a single iteration is performed by the
GA and a single generation of individuals is produced.
ii) The Training-aided step begins. the GA switches to the
Decision-directed adaptive modality. we have: Ã1 (j) = {ã1k (j − 1) : k = 1. Now. . working with a dierent parameterisation. 60 Application of Evolutionary Strategies to Channel Est. crossover and mutation op-
erators do not work. γT r ) are opportunely
chosen. Moreover. . This updating procedure is
light. .
In order to make clearer the proposed approach. In particular. and the GA parameters are reassigned as
. but this is reasonable because only small variations of the channel amplitude
and phase are to be tracked during the coherence period. PT r . the GA is initialized with the channel
matrices computed at the end of ii). The symbols employed in this
step to ll the data vectors A1 and A2 are the estimated symbols decided at the pre-
vious signaling period.

2 Particle Swarm Optimization-Assisted Channel Estimation
for STBC MIMO OFDM Systems
In this section.1 Overview of PSO
The Particle Swarm Optimization algorithm is a biologically-inspired stochastic optimiza-
tion technique developed by Eberhart and Kennedy in 1995 ([25]).
it is possible to say that it needs to be increased as SNR and Doppler spread
increase.
v) The Decision-directed updating step ends at the time t = LT + εT + Wcoh T . the terminology and the basic functioning idea of PSO is provided ([183. but. Then. population size PDD . Such
a parameter is linked to the Doppler spread and to the signal-to-noise ratio.
where Wcoh is the coherence time-window of the channel (expressed in number
of bits). in particular with GAs. its application to channel estimation will be considered. The PSO algorithm is initialized with
. imposing to the Gaussian generator
an updating standard deviation σup that actually is a system parameter.2.
4. It shares many similarities with evolutionary
computation techniques. motivated by social
behaviour of bird ocking or sh schooling. The footer DD means that the
GA parameterisation is related now to the Decision-directed adaptive step. The GA is re-initialized with the channel matrices computed at the
end of the coherence time-window. The
GA procedure produces a single population of individuals that are quite close to
the one chosen during the previous signaling interval. An
explicit mathematical link is very dicult to be obtained. as thumb rule. crossover probability
αDD = 0 and mutation probability γDD = 0.
184]).
4. for STBC MIMO OFDM Systems 61
follows: generation number GDD = 1. Such kind of population
is stochastically generated in Gaussian way. and re-parameterised in order to start again
with the Training-aided step ii).4.2 Particle Swarm Optimization-Assisted Channel Est.

The individuals of a society hold an opinion that is part of a belief space (the search
space). Following certain rules
of interaction. or only one of the rest of the population.2)
The velocity vid of the particle determines its movement. ring. It can be calculated as follows:
( ) ( )
t+1
vid = w · vid
t
+ c1 · ψ1 · ptid − xtid + c2 · ψ2 · ptgd − xtid (4. in MIMO Multi-Carrier Scenarios
a population of solutions (randomly or opportunely chosen) and searches the global opti-
mum of a real-valued function ( tness function ) dened in a given space ( search space )
by updating generations (even called epochs ). the individuals in the population adapt their scheme of belief to the ones
that are more successful among their social network. However. 62 Application of Evolutionary Strategies to Channel Est. this algorithm can be summarized as follows. Several
neighborhood topologies exist (e.. The position xid of the i-th particle in dimension d of that space is
determined by
xt+1 t t+1
id = xid + vid (4. unlike GA. some. a culture arises. Individuals share this opinion and may modify it by considering three aspects:
• the knowledge of the environment (its tness value)
• the individual's previous history of states (its memory)
• the previous history of states of the individual's neighborhood.
From the metaphorical point of view. in
which the individuals hold opinions that are closely related.g. full.
The denition of neighborhood congures the social network of the individuals. Over the time.
The continuous version of the PSO algorithm uses a real-valued multidimensional
space as belief space. PSO has no evolution
operators such as mutation and crossover. star) depending on whether an individual
interacts with all.3)
where:
t
• vid is the component in dimension d of the i-th particle velocity in iteration t
. The potential solutions (called particles ) y
through the problem space by following the current optimum particle.

PSO does not have genetic operators like crossover and mutation. Its value is usually kept
within the interval [−xmax max max
id . 189.
From the described procedure.
However. only
the global (or local) best individual gives out the information to others. The evolution only looks for the best solution. Compared
with GA. Both algorithms start with a certain population.
The PSO algorithm requires tuning of some parameters: the individual and sociality
weights (c1 . It is a one-way
information sharing mechanism.
A large inertia weight (w ) favors global search. In GAs. They also have memory.2 Particle Swarm Optimization-Assisted Channel Est. Compared with GAs.4. In PSO.
So the whole population moves like a one group towards an optimal area. Particle
update themselves with the internal velocity. chromosomes share information with each other. If inertia is used. while a small inertia weight favors local
search. xid ]. Both update the population and search for the optimum with random
techniques. Both theoretical and empirical studies are
available to help in selection of proper values ([185. 190. all the particles tend to converge to the best solution quickly even in the local
. it is clearer that PSO shares many common points with
GA.c2 ) and the inertia factor (w ). both have tness values to evaluate
the population. starting at an initial value close to 1. 191]). for STBC MIMO OFDM Systems 63
• xtid is the component in dimension d of the i-th particle position in iteration t
• c1 and c2 are constant weight factors
• pi is the best position achieved so long by particle i
• pg is the best position found by the neighbors of particle i
• ψ1 and ψ2 are random factors in the [0. 186. being xid the maximum value for the particle position.
t
A constraint (vmax ) is imposed on vid to ensure convergence. 188. it is sometimes decreased linearly during the iteration of the
algorithm. 187. which is also
important to the algorithm. 1] interval
• w is the inertia weight. the information sharing mechanism in
PSO is signicantly dierent.

In fact. . Aj (with j ∈ {1. The main dierences are
the application to STBC MIMO OFDM systems (instead of STBC MIMO MC-CDMA
systems) and the use of the PSO algorithm (instead of the GA) for optimization purposes. 64 Application of Evolutionary Strategies to Channel Est. and Hant = diag hant
0 . . ηN −1 ]T (with j ∈ {1. Nevertheless.
Therefore.
4.2. 2}) is the AWGN vector
(all vector components are independent and identically distributed with zero mean and
( )
variance σ 2 ). . The length of the
symbol period is much longer than that of a single-carrier system with the same data-rate. The basic func-
tioning is similar to the one described in the previous section.2 The Proposed PSO-Assisted Channel Estimation
In this sub-section. . 2}) are the vectors containing
the user BPSK symbols. Nj = [η0 .. a fading process slow enough to be considered block-fading in a single carrier
system might not be so in a system with an OFDM architecture. an MMSE channel estimation technique is described.4)

Y2 = −H (A2 )∗ + H (A1 )∗ + N2
A B
where Y1 and Y2 are N ×1 vectors. the eect of time-varying frequency se-
lectivity cannot be neglected in such systems as clearly stated in [194]. h ant
N −1 (with ant ∈ {A. . .
The usual hypothesis is that fading is at over each subcarrier and almost time-
invariant during two consecutive transmission period (i. . but its performance is usually much better than GA ([193]). h ant
1 . the
introduction of STBC into OFDM systems is not so straightforward. in MIMO Multi-Carrier Scenarios
version in most cases ([192]). the traditional received single-user OFDM signal samples acquired
at two consecutive symbol periods after the FFT-based coherent demultiplexing could be
expressed as follows:


Y1 = HA A1 + HB A2 + N1
(4.
In such a context. . η1 . Such a problem can
be solved by considering the OFDM channel non quasi-static over the space-time blocks:
.e. where hant
n is the complex channel coecient related to subcarrier
n and to the transmit antenna ant. B}) is the N ×N
diagonal channel matrix. the coherence time is much
greater than the symbol period). The computational complexity of PSO is comparable with
GA.

they could be used in the design and the
implementation of full smart recongurable terminals (RTs) capable of adapting their
transmission layer to a change of status of the network. the typology of the transceiver.Chapter 5
Adaptive and Optimized PHY-Layer
Recongurability
The implementation of the previously described multi-user detection and channel esti-
mation techniques has been done by taking into account some basic design issues typical
of Software Dened Radio (SDR) systems.. The channel state information (known in
67
. such as modularity and re-usability of devel-
oped software modules ([195]). meaning with the term status
the location and situation information.
Such a concept is well depicted in Fig. an increase of trac or interference load. 5. a change of channel
propagation conditions. and power
consumption constraints.). the computational capability. The reconguration action can belong to two main categories:
• a change of transmission modality (vertical handover)
• a reconguration of the parameters of the operating transmission modality in or-
der to optimize adaptively the Quality of Service (QoS) with respect to a situation
modication (e. etc.1. In such a way. together with the identication of the transmission
modes available.g.

aimed at
dynamically congure the PHY-layer in order to adapt itself to network situation. the modules are loaded in the transmitting and receiving devices.1: PHY-layer recongurability
the case of non-linear distorsions due to ampliers or estimated in the multipath fading
case) can be used to chose and parameterise a set of SDR transceiver modules. 68 Adaptive and Optimized PHY-Layer Recongurability
Figure 5. After
that. The considered approach
is completely dierent from the state of the art idea of embedding in a black box some
co-existing standard terminals provided with some switching protocol (even working on
. The dierent
PHY-layer congurations should be also able to provide the best performances in terms
of bit error rate.
The SDR-based implementation should guarantee the due degree of exibility and re-
congurability to the user terminal and to the entire network.

g. and multi-user detection algorithms (exam-
ples in literature have been already proposed for DS-CDMA systems ([196])).
or related to future on-going standards (e. S-UMTS or IEEE 802. and non-standard (e. Generally speaking. OFDM and MC-CDMA regarded as key
transmission techniques for new generation WLAN and cellular networks).
When a switching from a wireless connection (standard or not standard) to another
one is issued by the network manager (and physically managed by a proper middleware
layer as shown in Fig. But. the RT should be regarded as a full smart
terminal provided with extended adaptive recongurability managed both at RF. this means that. This can lead to the dynamic and
adaptive optimization of the RT for the generic transmission mode selected (see Fig. MIMO
systems represent other feasible examples of such capabilities ([197]).2). UMTS standard could protably exploit joint space and time
diversity provided by antenna arrays and rake receivers using co-ordinates adaptive array
optimization strategies.g. This is possible
now because smart antennas allow an eective software tuning of the radiation pattern
depending on channel conditions and interference load (also when bursts of interfering
signals are coming to the antenna with stochastic time of arrivals ([72])). the degrees of freedom allowed
to each transmission conguration will be exploited in order to optimize system perfor-
mances. A RT should be capable of taking in
charge a generic wireless transmission mode provided by means of dynamically linked SDR
libraries containing the executable code of the overall protocol stack elements managed
at terminal level. the design and the implementation of the SDR-based RT will be performed in adap-
tive and optimized way.g. and SDR pro-
cedure can actually work up to the IF stage ([198]). 5. in any
case. As an example. of UMTS. a new software-radio library containing the executable pro-
cedures implementing the new physical layer functionalities will be downloaded replacing
. The word generic means that the transmission mode should be both
standard. customized techniques for point-to-point connections).
starting from the physical layer level. while retaining the ba-
sic feature e.2)..Adaptive and Optimized PHY-Layer Recongurability 69
the basis of location/situation aware information). 5. channel equalization.. and
baseband level. In the standard transmission case.11 signals.

70 Adaptive and Optimized PHY-Layer Recongurability
Figure 5. situation/location awareness can be translated
into adaptive and optimized recongurability of the mobile terminal directly managed at
physical layer level. In such a sense. this task is not trivial. Of course. etc. interference load. (status
of the network) in order to provide the best QoS to the connecting user.
The result is an object oriented vision that allows to use the software modules as ba-
. thanks to SDR solutions.2: General overview of the recongurable architecture
the old one. The set
of SDR algorithms loaded and executed by a DSP (Digital Signal Processor) architecture
(for example) should dynamically adapt itself to the conditions of the new communica-
tion environment.
Such concepts have been taken into account during the software implementation phase. bandwidth
and power resources available. it is possible to say that the recongurability of the
terminal should be both adaptive and optimized with respect to the wireless context. transmission modalities. So.
In such a way. because the dynamic reconguration of the
mobile terminal should be optimized with respect to the channel conditions. mobile
terminals should be provided with an augmented exibility of the physical layer.

05). The step-
size parameter has been also reduced from the training to the decision-directed step in
order to force a fast convergence to the optimum weights during the former and to reduce
the impact of symbol errors in the latter.1 GA-Assisted MMSE MUD Receiver for MC-CDMA Systems 79
ii) RLS per-carrier MMSE MUD shown in [19]. Its values reported in the following are referred
to the training-aided modality. The resulting performances are really dierent. BER performances of
the proposed GA-assisted MMSE MUD receiver are almost coincident with ones yielded
by ideal MMSE MUD requiring perfect knowledge of channel gains for all SNR values.01 and 0.
The LMS and RLS algorithms have been modied in order to work in semi-adaptive
modality with the periodic transmission of a B -bit length training sequence. 6.3)
λ + (yn (i))∗ Pn (i) yn (i)
Pn (i + 1) = λ−1 {1 − Vn (i) (yn (i))∗ } (yn (i))∗ Pn (i) (6. it clearly outperforms both adaptive MMSE receivers based on deterministic
algorithms. RLS can approach ideal MMSE in case of
few transmitting users and relatively slow fading channels.
Simulation results in terms of BER are reported in Fig.
Moreover. 3. their values have been
reduced to a magnitude order less.1. BER results versus
SNR are plotted for a xed number of users (K = 9) in Fig.2)
k=1
Pn (i) yn (i)
Vn (i) = (6.1 and 6. It
represents a lower bound of the achievable performance.
. The weight-updating rules of the
RLS receiver are given as follows:
{K }
∑
ŵnRLS (i + 1) = ŵnRLS (i) + ckn aki − ŵnLM S (i) yn (i) (Vn (i))∗ (6. In the decision-directed modality. Nevertheless.2.4)
where λ is the so-called forgetting factor . Two curves related to the LMS are drawn with dierent step sizes
values (0.7. The sensitivity
to the parameter λ is very strong also for RLS (small variation of the parameter can
signicantly worse performance).
iii) The ideal MMSE MUD with optimum solution computed as in Eq. 6. as stated in [19]. Both LMS and RLS performances are strongly inuenced by the choice of the
parameters µ and λ.6.

The noisy spikes are due to the tness computation performed in the
. The proposed
technique (grey thick line) provides a good tracking of the ideal MMSE weight (dash-
dotted black line). N = 32. and SNR = 20 dB. where the amplitude and the phase of the estimated receiver
weight are shown for subcarrier 8. BER results versus users' number are reported for a xed SNR value of
20 dB. 80 Experimental Results
Figure 6.
A comparison between the tracking properties of the considered algorithms can be
done by observing Fig.2. It is worth noting that the performances of the proposed algorithm are very close
to those oered by ideal MMSE for whatever number of users. Such results conrm
the sub-optimality of steepest descent algorithms when the number of users increases.1: BER vs SNR for the simulated MC-CDMA receivers (with K = 9 users)
In Fig. Adaptive algorithms oer
instead performances substantially degrading for larger values of K. 6.3. 6. with K = 30.

During the decision-
directed step.
From the computational point of view. the computational burden of the GA is reduced to K · N · PDD elementary
operations.
The complexity of the LMS receiver is proportional to K · N. but it is generally less
performing than GA-based MMSE MUD and is strongly inuenced by the step-size pa-
.2: BER vs K for the simulated MC-CDMA receivers (SNR = 20 dB)
decision-directed step without any kind of averaging.6. LMS-based weight estimation (solid
black line) exhibits problems due to lag error ([178]) when the fast channel variations
occur. the proposed algorithm requires a number of
elementary operations equal to B · K · N · GT r · PT r during the training-aided step (see
[21] for a detailed analysis of the GA computational complexity).1 GA-Assisted MMSE MUD Receiver for MC-CDMA Systems 81
Figure 6.

. This observation also justies the choice to use just one
generation in the decision-directed step.10 for high SNR (e. The RLS detector oers better performances but the computational
requirement is comparable with the proposed algorithm.
Such a behaviour perfectly follows the remarks reported in [202]: it is often better to use
larger populations with less number of generations than small populations accompanied
by greater time for search.
SNR = 20 dB)
rameter setting. the Gaussian noise
..g.. For very low SNR (e.g. 15-20 dB) can eectively perform also for higher SNR (e. concerning the setting of the updating standard deviation σup during the decision-
directed step.g. 0-5 dB).3: Receiver weight estimated by the considered algorithms for subcarrier #8 (N = 32.
Finally. 10 dB). K = 15.
It has been shown that for a generation number higher than 10.
The choice of the GA parameters has been done through devoted simulation trials. the normalized mean
squared error between the estimated weights and the optimum weights does not decrease.
3. 82 Experimental Results
Figure 6. it has been observed that a unique value of σup made on the basis of Eq.. 30
dB) and lower SNR (e.g.

2.10 (dash-dotted
red line) are plotted for each SNR value. In particular.2 GA-Assisted ML MUD Receiver for Multi-Rate VSL MC-CDMA Systems 83
Figure 6. Such a
behaviour is clearly reported in Fig.6. where the best value of σup obtained through
simulations (solid blue line) and the value of σup obtained by using Eq. some selected
simulation trials have been performed by using an equivalent baseband simulator of a
multi-rate MC-CDMA downlink transmission system.4. 6. the multipath fading channel modelling and parameterisation has been
performed through some experimental data reported in [203] and related to 1. In order to dene more realistic
simulation trials. 3.4: GA updating standard deviation
is clearly predominant and the updating standard deviation should be lowered.95 GHz
3GPP (3G-Partnership Project) transmission scenarios.2 GA-Assisted ML MUD Receiver for Multi-Rate VSL MC-
CDMA Systems
In order to assess the GA-based MUD algorithm proposed in section 3.
6. a 4-paths Rayleigh
fading channel related to an urban vehicular scenario has been simulated by using a tapped
.

84 Experimental Results

delay line with coherence bandwidth equal to 1.25 MHz and Doppler spread equal to 125

Hz. The users' amplitudes Au have been chosen in order to maintain the per-bit signal-to-

iii) Linear MMSE receiver followed by a SIC stage, as described in [206] and [207].

The MMSE SIC is based on a per-user successive decoding with an arbitrary,

but xed order. In the multi-rate VSL MC-CDMA context, it is reasonable to

assume that users are received starting from the slower to arrive to the faster.

iv) Curves of the single-user bound achieved for the dierent users classes. The

single-user bound has been derived by simulating a single-user MC-CDMA sys-

tem (therefore interference-free) using a number of subcarriers equal to eective

Jgen = 10.2 GA-Assisted ML MUD Receiver for Multi-Rate VSL MC-CDMA Systems 87
Figure 6. MMSE-pcPIC.
rb3 = 256 Kbps).
It is possible to note that the GA-assisted ML MUD performs better than sub-optimum
MMSE. In particular. dhamm = 2
processing gain of the intended class (N1 = 4 for the class 1. the proximity of the related BER curve to the single-user bound
is dramatically evident in Fig. MMSE) and by the single-user bound: user class #3 (N3 = 16. the single-user bound tends to depart from all sub-optimal al-
. N2 = 8 for the class
2 and N3 = 16 for the class 3). MMSE-pcPIC.6. urban 3GPP vehicular channel. MMSE-SIC. and MMSE-SIC MUD algorithms for all the considered user
classes.5. 6. As the users' data rate decreases and.7: BER results vs Eb /N0 provided by the dierent MUD algorithms assessed (GA-assisted
ML MUD. therefore. the
processing gain increases. The output of the coherent FFT demux is then
combined on the basis of the MRC criterion that is the optimal (ML-based)
criterion in the case of single-user transmission.

2.3 shows the order of compu-
tational complexity for each MUD algorithm assessed (second column).
by observing BER curves of Fig.3: Analysis of computational complexity of the dierent MUD algorithms assessed
As far as computational issues are concerned. 207]
Table 6.56 · 102 3.
GA-assisted ML N + Ppop +
Ppop = 137. The reader can note
that the computational burden of the proposed GA-assisted ML MUD increases only by
.7.9 · 10−3
MMSE-pcPIC
U2 · N 4.6 and Fig. the number of
elementary operations required by each algorithm to derive a sub-optimal solution to the
considered problem (third column).4 · 103 (Jgen = 10. because the single-user bound drawn in Fig.
# of Elementary
# of Elementary Operations Needed to
MUD Algorithm Order of Computational Operations Needed to Compute the Problem
Assessed Complexity Compute the Problem Solutions Normalized
Solutions with Respect to
Theoretical ML MUD
1. the number of ele-
mentary operations normalized with respect to the corresponding value required by the
theoretical ML MUD exploring the full search space (equal to 2U ).1 · 10−2
detection (α + γ) Jgen Ppop
γ = 0. This is due to the successive
cancellation order followed in the simulations.25 · 10−2
[206.7 for user class 3 are almost coincident. and nally in the fourth column. 88 Experimental Results
gorithms. 6. 6. 6. it is possible to note that the perfor-
mance improvement yielded by GA-assisted ML MUD even becomes more relevant. 6. Tab. This is not unexpected.1 · 103 6.25 · 10−2
[204]
MMSE-SIC
U2 · N 4. as stated in [67]. α = 0. However.5-6. It is
worth noting that that BER performances of MMSE-pcPIC and MMSE-SIC algorithms
drawn in Fig. 6.7 is
actually a lower bound also on theoretically-optimal ML MUD.9.01)
MMSE [15] U ·N 2.1 · 103 6.

The following parameters
have been xed: number of subcarriers N = 8. the Rural Area channel
model (identied by the acronym RAx) has been used. transmission data rate rb = 1024 Kbps. 6.3 Adaptive MBER MUD Detector for STBC MIMO MC-CDMA
Systems
The adaptive MBER MUD algorithm for STBC MIMO MC-CDMA systems presented in
section 3. If the number of eective users U increased.
The simulated mobile transmission channel has been modeled according to the guidelines
issued by the 3GPP standardization group in [208]. In the last column of
Tab.3 Adaptive MBER MUD Detector for STBC MIMO MC-CDMA Systems 89
less than one order of magnitude with respect to that one required by MMSE MUD and
is slightly reduced with respect to MMSE-pcPIC and MMSE-SIC.3 has been tested by means of intensive simulations.3.
. In particular.1 -4
3 0.
Tap Number Delay [µs] Amplitude [dB]
1 0 0
2 0.2 -8
4 0.4 -16
6 0.
6.3 -12
5 0. 6. the noticeable reduction of computational eort with respect to theoretical ML
MUD is clearly shown.5 -20
Table 6.4. such a computational
saving would be even more glaring. Its tapped delay line model is
summarized in Tab.
Hadamard-Walsh sequences for CDMA spreading (such a choice is suggested by some
widely-cited references about MC-CDMA like [15]).6.4: Channel model (RAx)
The corresponding coherence bandwidth is around 2 MHz and the considered Doppler
spread is equal to 100 Hz.

Fig. namely
light load).
clearly sub-optimum in the multi-user case.
Simulation results in terms of BER have been shown in Fig. 6.
.n 2 2.n
being ξ the step-size parameter of the LMS procedure.5)
ŵLM S (i + 1) = ŵLM S (i) + ξ {ãk (i) − ŵLM S (i) Y (i)} YH (i)

2.n 2.9 (K =4 users. periodic training and decision-directed updating within a channel
coherence window) has been adopted also for the MMSE adaptive receiver. 6. The training period has been chosen equal to the coherence time of the channel
(approximately equal to the inverse of the maximum Doppler shift. It is the typical single-user receiver.
equivalently.
The adopted training sequence is a pseudo-random binary vector taken by the Hadamard-
Walsh set.. the EGC receiver coherently recombines diversity
branches without amplitude weighting.
the following receivers have been considered:
i) The EGC receiver shown in [176]. obtained assum-
ing the ideal CSI knowledge.
ii) The LMS adaptive implementation of the linear MMSE MUD receiver shown
in [85] and [176]. The
weight updating rule for adaptive MMSE is therefore given as follows:

 { }
ŵ1.8 (K =2 users. 6. 90 Experimental Results
A known sequence of 32 bits has been adopted for the periodic training-aided modality. has been adopted as single-user bound for the
tested performances (it is known from [15] that MRC reception is theoretically
optimum for single-user MC-CDMA systems).
namely full load). Such receiver assumes the perfect knowl-
edge of the CSI. therefore 10 msec or. The same updating strategy used for the LMS-based MBER
receiver (i.
In order to compare the proposed MBER algorithm with state of the art approaches. and Fig.n
LM S
(i) Y (i) YH (i)
(6.n (i) + ξ ãk1 (i) − ŵ1.n
LM S LM S
(i + 1) = ŵ1.e. 10240 bit periods). In this case.10 (K =7 users. namely half load).
iii) The MRC reception of the single-user MAI-free signal ([176]).

and
ξ) have been experimentally chosen in order to achieve the best results in terms of BER
both for the LMS-based MBER MUD and MMSE MUD. The sensitivity to parameterisa-
tion is a well-known limitation of deterministic gradient adaptive optimization approaches
([178]). step-size
parameters µ and ξ are more depending on the users number K and subcarriers' number
N. In fact.3 Adaptive MBER MUD Detector for STBC MIMO MC-CDMA Systems 91
Figure 6.
.27). In particular.6. N = 8 subcarriers. it has been observed that the step-size should be decreased for increas-
ing values of K and N. Moreover. On the other hand. Experimental trials evidenced that the parameter ρη is directly linked with trans-
mission SNR (as clearly understandable by Eq. the parameterisation has been dierentiated in the
training-aided modality with respect to the decision-directed updating modality.8: BER results vs SNR provided by the dierent signal detection algorithms assessed (with
K = 2 users. 3. NT x = NRx = 2 antenna elements)
The numerical values of the parameters controlling the weight updating (µ. ρη .

In order to avoid the recursive computation of a noisy gradient.
As far as BER results are concerned. 6. 6. On the other hand.9 that
when the user load is light (K = 2) or half (K = 4) the BER curve provided by
the MBER MUD receiver is quite close to the single-user bound. the decision-directed phase is
only devoted to track the small variations of the channel impulse response in the presence
of noisy symbol decisions.
the step size is consistently decreased during decision-directed updating with respect to
the training phase. 92 Experimental Results
Figure 6. clearly outperforming
EGC and LMS-based MMSE MUD. one can note from Fig. N = 8 subcarriers.8 and Fig. NT x = NRx = 2 antenna elements)
the training phase should ensure a fast convergence of the weights to the optimal value.9: BER results vs SNR provided by the dierent signal detection algorithms assessed (with
K = 4 users.
exploiting a sequence of known bits. Poor BER performances provided by EGC receiver
.

6. N = 8 subcarriers. On the other hand. related to
the full load case with K = 7). Moreover. the global detection noise (including AWGN and multi-user interference) is
getting more and more Gaussian-distributed and. As users' number K
increases.10: BER results vs SNR provided by the dierent signal detection algorithms assessed (with
K = 7 users.3 Adaptive MBER MUD Detector for STBC MIMO MC-CDMA Systems 93
Figure 6. the MBER MUD approach still provides better results
than MMSE one. the two curves are closer one with respect to another than
in Fig. therefore. 6. Nevertheless. 6. 6. NT x = NRx = 2 antenna elements)
conrm.
as the number of users approaches the maximum allowable value (see Fig.8 and Fig. optimizing the receiver with
. in all the tested cases. the unsuitability of single-user detection in the presence of
multi-user transmission and frequency-selective channel distortions. they are farer from the single-user bound.
Such kind of behavior has been already noted by Chen in [59] for the MBER MUD applied
to the DS-CDMA case and can be motivated by statistical reasons.10.9.

however obtained with an aordable
computational eort. transmission data rate rb = 1024
Kbps. Moreover.4 have been
evaluated by means of intensive simulation trials in a Rayleigh fading channel xing the
following parameters: number of subcarriers N = 8. one can note that
the computational order of the adaptive LMS-based MBER MUD is O (K).1 MHz.34. analyzing the dierent terms of Eq. 3. Considering that
the adaptive LMS-based MBER MUD always outperforms in terms of measured BER
both EGC and LMS-based MMSE.2. In case of increasing number of users.10 is a lower bound also on theoretically
optimum ML detection as it does not take into account the presence of the multi-user
interference. 6. 94 Experimental Results
respect to the MMSE provides very close results to optimizing on BER. coherence bandwidth of the channel 2. therefore
linear with respect to the users number.
6.3.4 Adaptive MCBER MUD Detector for STBC MIMO MC-
CDMA Systems with GA-Assisted MMSE Channel Estima-
tion
The performances of the LMS-based MCBER detector presented in section 3. it is worth noting that the theoretical
( )
ML detection is characterized by an unaordable computational order O 2K that is
exponentially-growing with the users' number K. Doppler spread of the channel 100
Hz. In fact. it is getting more and more dicult
for a linear receiver to approach the single-user bound. the advantages of the proposed detection scheme are
evident at a rst glance. The same computational order characterizes
both the LMS-based MMSE MUD and the single-user EGC combiner. the proposed LMS-based MBER MUD receiver represents a step ahead with
respect to state of the art linear combining receivers.
As far as computational issues are concerned. as mentioned in sub-section 3. On the
contrary.8-6. Two test cases have been considered: the most theoretical case related to the MCBER
. the
single-user bound curve depicted in Fig. The same problem occurs when the
theoretical MBER criterion is considered.

1 (a block diagram of the resulting transceiver scheme is depicted in Fig. it is possible to test the eectiveness of the channel estimation strategy
adopted together with the impact of non-ideal channel estimation on MCBER perfor-
mances.11). 6.11: Block diagram of the STBC MIMO MC-CDMA system with GA-assisted channel estimation
In such a way.
ii) The MMSE MUD receiver supported by the GA-assisted channel estimation.
iii) The LMS adaptive implementation of MMSE receiver shown in [176] and [181]. the BER curve obtained by MRC detection in the single-user case has
.6.
Figure 6.
In order to verify the eectiveness of the proposed approach. namely:
i) The ideal MMSE MUD receiver exploiting ideal CSI knowledge ([85. 176]).
iv) The single-user EGC receiver considered in [176].4 Adaptive MCBER MUD Detector for STBC MIMO MC-CDMA Systems with GA-Assisted
MMSE Channel Estimation 95
detector exploiting the ideal CSI knowledge and the more realistic case related to the
MCBER detector supported by the GA-assisted channel estimation described in section
4.
As lower bound. other state of the art
receivers have been considered for comparison.

population size PT r = 30. 96 Experimental Results
been considered (i. respectively. Moreover.
In general. Therefore the overhead due to the insertion of the training sequence
equals to less than 1.9.12.12-6.
Fig..
Moreover. Such behaviour is not unexpected. 6. and Fig.
Three dierent scenarios including K = 2. for an increasing number of users. BER curves related to ideal MMSE and
MCBER tend to become closer to each other and more distant with respect to the single-
user bound.8%. 6. As number of users K increases.e. mutation probability γDD = 0
• Training sequence length B = 32 bit
• Coherence window length Wcoh = 1800 bit (according to the Jake's model for Rayleigh
channels [201]). K = 4.
crossover probability αDD = 0. 6. supposing the absence of multi-
user interference). The GA optimizer has been parameterised as follows:
• Training-aided step: generation number GT r = 30. and K = 6 users have been considered.
crossover probability αT r = 0. population size PDD = 10.14. 6. Such a last improvement
is clearly evident for K = 2 and K = 4 users. the optimal single-user detection.01
• Decision-directed step: generation number GDD = 1. optimizing the receiver with respect to the MMSE
provides very close results to optimizing on BER.13. mutation probability γT r = 0.
It can be seen that in all scenarios the proposed LMS-based MCBER detector with and
without ideal CSI knowledge clearly outperforms both EGC detector and LMS-based
MMSE adaptive detector that exhibit a nasty error oor as the number of users increases. the proposed MCBER detector exploiting ideal CSI knowledge yields perfor-
mances that are better than those ones of ideal MMSE detector. the global
detection noise (including AWGN and multi-user interference) is getting more and more
Gaussian-distributed and. therefore. the single-user bound curve
depicted in Fig.14 is a lower bound also on theoretically optimum ML detection
. whereas it becomes slighter for K = 6 users.
The corresponding BER curves vs SNR are shown for all the tested receivers in Fig.

In case of
increasing number of users. it is getting more and more dicult for a linear receiver to
approach the single-user bound. LMS-based MMSE multi-user detectors and EGC receiver
(with ideal CSI knowledge) for N = 8 subcarriers and K = 2 users
as it does not take into account the presence of the multi-user interference.4 Adaptive MCBER MUD Detector for STBC MIMO MC-CDMA Systems with GA-Assisted
MMSE Channel Estimation 97
Figure 6.
.12-6.
Focusing the attention on the eectiveness of the proposed GA-assisted channel esti-
mation methodology. 6. The eects of non-ideal
channel estimation are more evident for higher number of users (K = 6) and therefore in
the presence of higher level of multiuser interference.12: BER performance yielded by LMS-based MCBER and ideal MMSE (with ideal CSI knowl-
edge and GA-assisted channel estimation). it is possible to see from Fig.14 that the MCBER detector
supported by non-ideal channel estimation performs very close to MCBER detector ex-
ploiting the ideal channel knowledge for K=2 and K=4 users.6.

The results shown in Fig.13: BER performance yielded by LMS-based MCBER and ideal MMSE (with ideal CSI knowl-
edge and GA-assisted channel estimation).
Such results can be regarded as a further conrmation of the goodness of the proposed
GA-assisted channel estimation approach. is decreasing
with SNR and exhibits satisfactory values (e..g.15
highlight that such a variance. 6. LMS-based MMSE multi-user detectors and EGC receiver
(with ideal CSI knowledge) for N = 8 subcarriers and K = 4 users
The most relevant fact able at conrming the correctness of the conducted analysis is that
the proposed MCBER detector always outperforms MMSE MUD when working together
in the same conditions of channel knowledge.
The eectiveness of the GA-assisted channel estimator has been tested in terms of error
variance in the worst case of interference load (i. K = 6). computed on the overall channel coecients.
.e.. lower than 10−2 for SNR > 10 dB). 98 Experimental Results
Figure 6.

the MCBER
criterion is theoretically closer to optimality than MMSE and simulation results shown in
Fig. the developed adaptive MCBER MUD algo-
rithm is also less demanding from a computational viewpoint than ideal MMSE. 209]) and it is comparable with the LMS-based adaptive implemen-
tation of MMSE (which is linear again). LMS-based MMSE multi-user detectors and EGC receiver
(with ideal CSI knowledge) for N = 8 subcarriers and K = 6 users
As far as computational issues are concerned. 85.14 conrm this claim.6.12-6. 176. Moreover. although
. The
computational eort is therefore reduced with respect to the ideal MMSE detector which
is O (K 3 ) ([65. 6. The reduction of the computational complexity
is one of the main advantages yielded by the proposed approach.4 Adaptive MCBER MUD Detector for STBC MIMO MC-CDMA Systems with GA-Assisted
MMSE Channel Estimation 99
Figure 6.14: BER performance yielded by LMS-based MCBER and ideal MMSE (with ideal CSI knowl-
edge and GA-assisted channel estimation). In fact. current literature claims that MCBER
criterion has a computational order which is linear with the number of users ([91]).

About computational complexity of the GA-assisted MMSE channel estimator.
. where ε > 1.
NT x = 2 and NRx = 1 antenna elements
requiring the same knowledge of the channel state information..g. 100 Experimental Results
Figure 6. the
computational burden of the GA is reduced to K · N · PDD elementary operations to be
executed during a signalling period T. During the decision-directed step. Such a computational requirement is comparable
with that one involved by state of the art STBC channel estimation algorithms (see.g. The value assigned to ε mainly depends on the computational
power of the signal-processing device employed. it is
possible to say that GA requires a number of elementary operations to derive a solution
that is equal to νop = (α + γ) · G · P (see. N = 8 subcarriers. Their execution time is equal
to εT . e. e.. Thus. B · K · N · GT r · PT r elementary
operations are required during the training-aided step. [21]).15: Variance of the channel estimation error measured for K = 6 users.

crossover. and mutation probabilities have been found in order to as-
sure the best tradeo between achieved results and computational load.
population size.
As far as the parameterisation of the GA-based optimizer is concerned. xing the number
of users K. 182. it is possible
to say that formal methodologies targeted to nd an optimal parameterisation of ge-
netic procedures are not available. in case of large population. At the end of the
simulation trials devoted to parameterisation. In literature. numerical values for generation number. Then. In particular. From the parameters selection phase. On the
other hand. 210.6. it has been noted that
LMS-based MCBER detector is characterized by a reduced sensitivity to parameterisa-
tion with respect to state of the art LMS-based MMSE MUD. it has been ob-
. performed by keeping into account the
major guidelines pointed out in [202] that basically are these two ones:
a) the population size should be suciently large in order to have a conveniently-
dimensioned space search
b) the number of generations should be appropriately assigned in dependence of
the population size. the step-size λ is substantially invariant with respect to SNR values.
an intermediate SNR equal to 15 dB has been assumed as reference value. The step-size parameter λ of both LMS-based algorithms (MMSE and MCBER)
has been chosen empirically for each scenario in order to minimize the overall BER over
the various SNR values. So GA parameters have been selected
by means of explicitly-devoted simulation trials. 211]). there are only some interesting heuristic
analysis like the one proposed by Tsoi in [202]. some notes about algorithmic parameterisation are now pro-
vided.4 Adaptive MCBER MUD Detector for STBC MIMO MC-CDMA Systems with GA-Assisted
MMSE Channel Estimation 101
[85. LMS-based MMSE multi-user detector would require a dierent value of λ
for each SNR in order to provide satisfactory BER performances. too strict limit for the search time can force algorithm
to stop without having enough time to realize its search possibility.
To conclude this section.
In fact. and the best
parameterisation has been derived by simulations for this value. Indeed.

and PSO-assisted).5 MMSE PSO-Assisted Channel Estimator for STBC MIMO
OFDM Systems
The goodness of the PSO-assisted channel estimation technique described in section 4.e. 102 Experimental Results
served by other simulations that the GA parameterisation chosen for the reference SNR
is very close to the best also for higher and lower SNR values. ideal.1 MHz.1 and the traditional LMS and RLS steepest
descent algorithms presented in [143].. GA-assisted. 216]).6 (instead of the one reported in Eq. RLS-
assisted. was to use the following combiner:


Â1 = (HA (t))∗ Y1 + HB (t + 1) (Y2 )∗
(6.
The eectiveness of the proposed approach has been tested by considering the MRC
receiver proposed in [194] with dierent CSI knowledges (i. LMS-assisted. The rest of the algorithm
remains unchanged.6)

Â2 = (H (t))∗ Y1 − H (t + 1) (Y2 )∗
B A
.1). 213. 214. Such a modication can be easily done by considering the tness function
expressed in Eq. Abreu et al. 4.
The solution proposed by Abreu et al. Doppler spread of the
channel 10 Hz. instead of STBC MIMO MC-
CDMA systems. transmission data
rate rb = 1024 Kbps.
Its performances have been compared with those one oered by the GA-assisted channel
estimation technique proposed in section 4. noticed that the estimates ob-
tained with the conventional linear decoder for Alamouti's scheme are no longer orthog-
onal in the presence of a non-quasi-stationary fading process. 4. coherence bandwidth of the channel 2. The GA-based algorithm has been opportunely
modied to operate in STBC MIMO OFDM systems.
6.2
have been tested through intensive simulation trials in a Rayleigh fading channel xing
the following parameters: number of subcarriers N = 32 and N = 64. Such a situation leads to
have error oors at higher SNR values (as already noticed in [212. It conrms the reduced
sensitivity to parameterisation of GA procedures. 215.

RLS-assisted.2.1). LMS-
assisted.18 highlight that such a variance.
The same behaviour has been observed in terms of error variance in the case with 32
subcarriers. It conrm the superiority of the PSO algorithm with respect to GA
in this application (as already described in subsection 4. 6. GA-assisted. The best performances are provided by the detector with the PSO-assisted
channel estimator. The results shown in Fig. and PSO-assisted channel estimation) for N = 32 subcarriers
It can be seen that in both scenarios the proposed GA. Both MRC receivers assisted by channel estimation tech-
niques based on evolutionary strategies perform really close to the one with ideal CSI
knowledge. computed on
the overall channel coecients.and PSO-based receivers clearly
outperforms both LMS and RLS steepest descent algorithms that exhibit a nasty error
oor as the SNR increases.6 (with ideal CSI knowledge. 6. 104 Experimental Results
Figure 6.16: BER performance yielded by MRC detector of Eq. is decreasing with SNR and it is lower for the PSO-assisted
.

and RLS-based
approaches especially when the impact of MAI becomes predominant in limiting trans-
mission capacity.1 has formulated a novel semi-adaptive GA-based approach for MMSE MUD
in MC-CDMA systems transmitting information over time-varying fading channels. it does not require any channel estimation but just a short training sequence
periodically transmitted. The
proposed algorithm evidenced some advantages with respect to other state of the art solu-
tions. novel
multi-user detection techniques for multi-carrier CDMA systems and the application of
evolutionary strategies to channel estimation in MIMO multi-carrier scenarios have been
considered. in order to better counteract noise eects. First.
Section 3. In particular. The main targets have been the study and
development of novel techniques for physical layer optimization of diversity based smart
terminals in the context of next-generation wireless communications. Other
107
. Then. simulation results achieved in terms of BER evidenced
a near-ideal behaviour of the proposed algorithm. All the formulated algorithms have been implemented in a modular software
architecture. the methods and results presented in this thesis are reviewed and sev-
eral lines for future research are suggested.
Future works could introduce an average operation in the computation of the tness func-
tion also in the decision-directed step. in order to use them in an adaptive and optimized recongurable scenario.Chapter 7
Conclusions and Future Works
In this Chapter. outperforming LMS.

g. in the case of full user load.3 presented a novel adaptive LMS-based MBER multi-user detection ap-
proach for MIMO STBC MC-CDMA systems transmitting information over time-varying
multipath fading channels. for this reason. might be an interesting subject of future
works. genetic algorithms.
Section 3.
like..
Future works could concern with some relevant aspects not faced in the present work. In this case. Such relevant results have been achieved by spending
an aordable computational burden. provided that GA parameters are carefully tuned. the
application of RLS to MBER reception should be investigated as well.
.. e.
Future research works should be devoted to the exploitation of more ecient optimiza-
tion strategies based on the stochastic gradient (e.
Section 3. 108 Conclusions and Future Works
intensive simulation trials should be done in presence of time varying channels character-
ized by very fast multipath fading. The per-
formance improvement achieved with respect to state of the art interference cancellation
schemes is clearly demonstrated. joint
GA-based channel estimations and symbol detection. eects of system non-linearities. eects of non-ideal channel estimation. BER curves of LMS-
based MMSE and LMS-based MBER become closer than in other test cases. This can
be justied by the statistical properties of global detection noise that becomes more and
more Gaussian as the users' number increases.g. etc.
Experimental results evidenced that. the
denition and the recursive computation of the Kalman gain vector do not appear as
trivial as in the MMSE case and. Considering again steepest descent-based optimization methodologies. particle swarm
optimization). The proposed algorithm evidenced some advantages in terms
of improved BER performances with respect to state of the art receivers that rely on the
mean squared error minimization (MMSE) and on single-user diversity combining (EGC).2 proposed the use of GAs in the context of the multi-user detection of
multi-rate variable-spreading-length MC-CDMA signals transmitted over mobile downlink
channels. Results shown evidenced a promising quasi-optimal behaviour of the proposed
GA-based MUD algorithm.

Conclusions and Future Works 109

Section 3.4 investigated a multi-user detection strategy inspired to the concept of con-